how much do social media sites shape public opinion essay

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How does social media use influence political participation and civic engagement? A meta-analysis

2015 paper in Information, Communication & Society reviewing existing research on how social media use influences measures such as voting, protesting and civic engagement.

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by John Wihbey, The Journalist's Resource October 18, 2015

This <a target="_blank" href="https://journalistsresource.org/politics-and-government/social-media-influence-politics-participation-engagement-meta-analysis/">article</a> first appeared on <a target="_blank" href="https://journalistsresource.org">The Journalist's Resource</a> and is republished here under a Creative Commons license.<img src="https://journalistsresource.org/wp-content/uploads/2020/11/cropped-jr-favicon-150x150.png" style="width:1em;height:1em;margin-left:10px;">

Academic research has consistently found that people who consume more news media have a greater probability of being civically and politically engaged across a variety of measures. In an era when the public’s time and attention is increasingly directed toward platforms such as Facebook and Twitter, scholars are seeking to evaluate the still-emerging relationship between social media use and public engagement. The Obama presidential campaigns in 2008 and 2012 and the Arab Spring in 2011 catalyzed interest in networked digital connectivity and political action, but the data remain far from conclusive.

The largest and perhaps best-known inquiry into this issue so far is a 2012 study published in the journal Nature , “A 61-Million-Person Experiment in Social Influence and Political Mobilization,” which suggested that messages on users’ Facebook feeds could significantly influence voting patterns. The study data — analyzed in collaboration with Facebook data scientists — suggested that certain messages promoted by friends “increased turnout directly by about 60,000 voters and indirectly through social contagion by another 280,000 voters, for a total of 340,000 additional votes.” Close friends with real-world ties were found to be much more influential than casual online acquaintances. (Following the study, concerns were raised about the potential manipulation of users and “digital gerrymandering.” )

There are now thousands of studies on the effects of social networking sites (SNS) on offline behavior, but isolating common themes is not easy. Researchers often use unique datasets, ask different questions and measure a range of outcomes. However, a 2015 metastudy in the journal Information, Communication & Society , “Social Media Use and Participation: A Meta-analysis of Current Research,” analyzes 36 studies on the relationship between SNS use and everything from civic engagement broadly speaking to tangible actions such as voting and protesting. Some focus on youth populations, others on SNS use in countries outside the United States. Within these 36 studies, there were 170 separate “coefficients” — different factors potentially correlated with SNS use. The author, Shelley Boulianne of Grant MacEwan University (Canada), notes that the studies are all based on self-reported surveys, with the number of respondents ranging from 250 to more than 1,500. Twenty studies were conducted between 2008 and 2011, while eight were from 2012-2013.

The study’s key findings include:

• Among all of the factors examined, 82% showed a positive relationship between SNS use and some form of civic or political engagement or participation. Still, only half of the relationships found were statistically significant. The strongest effects could be seen in studies that randomly sampled youth populations.
• The correlation between social-media use and election-campaign participation “seems weak based on the set of studies analyzed,” while the relationship with civic engagement is generally stronger.
• Further, “Measuring participation as protest activities is more likely to produce a positive effect, but the coefficients are not more likely to be statistically significant compared to other measures of participation.” Also, within the area of protest activities, many different kinds of activities — marches, demonstrations, petitions and boycotts — are combined in research, making conclusions less valid. When studies do isolate and separate out these activities, these studies generally show that “social media plays a positive role in citizens’ participation.”
• Overall, the data cast doubt on whether SNS use “causes” strong effects and is truly “transformative.” Because few studies employ an experimental design, where researchers could compare a treatment group with a control group, it is difficult to claim causality.

“Popular discourse has focused on the use of social media by the Obama campaigns,” Boulianne concludes. “While these campaigns may have revolutionized aspects of election campaigning online, such as gathering donations, the metadata provide little evidence that the social media aspects of the campaigns were successful in changing people’s levels of participation. In other words, the greater use of social media did not affect people’s likelihood of voting or participating in the campaign.”

It is worth noting that many studies in this area take social media use as the starting point or “independent variable,” and therefore cannot rule out that some “deeper” cause — political interest, for example — is the reason people might engage in SNS use in the first place. Further, some researchers see SNS use as a form of participation and engagement in and of itself, helping to shape public narratives and understanding of public affairs.

Related research: Journalist’s Resource has been curating a wide variety of studies in this field. See research reviews on: Effects of the Internet on politics ; global protest and social media ; digital activism and organizing ; and the Internet and the Arab Spring . For cutting-edge insights on how online organizing and mobilization is evolving, see the 2015 study “Populism and Downing Street E-petitions: Connective Action, Hybridity, and the Changing Nature of Organizing,” published in Political Communication .

Keywords: social media, Facebook, Twitter

About The Author

John Wihbey

I’m right, you’re wrong, and here’s a link to prove it: how social media shapes public debate

Senior Lecturer, School of Communication, International Studies and Languages, University of South Australia

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Collette Snowden does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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Social media has revolutionised how we communicate. In this series, we look at how it has changed the media, politics, health, education and the law.

Once upon a time different political perspectives were provided to the public by media reporting, often through their own painstaking research.

If an issue gained attention, several perspectives might compete to inform and shape public opinion. It often took decades for issues to make the transition from the margin to the centre of politics.

Now, within minutes of any event, announcement or media appearance, we are able to get those perspectives thousands of times instantly via social media. There are constant reactions and debates, often repeating the same arguments and information.

It’s the communication equivalent of being at a football match compared to a dinner party. While meaningful exchanges between individuals are possible on social media, there’s so much noise that it’s difficult to make complex arguments or check the validity of information.

Social media is a superb medium for immediacy, reach and intensity. This makes it a great asset in situations where timeliness is important, such as breaking news. But it has serious limitations in conveying tone, nuance, context and veracity.

The pros and cons of social media

The ability for people to engage in arguments at a distance on social media has revealed an appalling lack of civility in many deep pockets of misogyny, ethnic antipathy, and general intolerance for difference.

These are attributes of users, not the technology, but social media gives them a volume that they otherwise would not have. But these loud, often angry, voices also prevent many more people from taking advantage of its participatory potential.

The level of hostility encountered in many debates is a powerful deterrent for many. Nonsense and profundity, truth and fabrication, have equal rights on social media. It can be a frustrating and bewildering place, and a great waster of time.

Nonetheless, with the dedication and commitment of a few passionate supporters, small and more marginalised groups are able to create a public presence that previously would have required years to establish through community meetings, lecture tours, fundraising events and lobbying.

A group like the Free West Papua movement, established in 1965 but outlawed by the Indonesian government, has successfully used social media to generate global support.

Other cause-related issues – such as animal-rights activism – that were previously confined to the margins of public attention have benefited from the greater reach social media allows.

Communications technology has also enabled social media to amplify many debates about long-standing issues, such as domestic violence, by allowing people to share their stories and engage in debates. These in turn can place pressure on politicians to act and contribute to critical offline discussions.

Just how powerful is it?

The influence of social media on politics and public perception is indisputable, but the extent of that influence is yet to be determined.

While social media was initially dismissed by some politicians as trivial , few make that argument now. Social media analytics are scrutinised with the same intensity as polls, and politicians and political parties follow social media exchanges closely.

But while political organisations and the media emphasise the volume of emotive, ephemeral and instantaneous messages produced for social media, they increasingly overlook context, complexity and causation.

So, the Australian election result , for example, was a surprise, particularly the level of support for One Nation. Similarly, the UK referendum result on its membership of the European Union was a shock. The US election is covered as though the tweets of candidates are providing the policy settings for an entire administration. The outcome of a referendum in Colombia was a surprise.

These outcomes are not directly caused by social media – they’re far too complex to make that claim – but social media is a powerful contributing factor.

But we should be aware of its limitations

There is a clear danger in focusing on social media as the primary agenda-setting medium for public debates while ignoring the deeper, complex social roots of conflicting ideas or positions.

While social media may create awareness, real political change requires actual decision-making, which takes time and reflection.

Social media debates on politics quickly devolve into binary positions, between which repetitive messages bounce back and forth, often without resolution. The marriage equality issue in Australia is an example of an issue that has benefited from social media communication. But without a strong political will for change, the issue has stalled as real politics have come into play.

Politicians and organisations now devote considerable time to social media. Shouting at each other, and exulting in the ability to gather followers, be liked, retweeted or shared, the danger is in being oblivious to the people who either do not use social media, or use it sparingly or infrequently.

Consequently, social media activity gives a greater illusion of impact precisely because of the attention it is given by people spending so much time on it.

News, gossip, and political debates occur in all human societies. Whether it’s tribal councils (so creatively co-opted for reality television), the Roman Forum, Town Hall debates (now televised to global audiences), the public bar, the coffee shops of Europe , and so on, social communication about politics is hardly new.

The need and desire for people to discuss decision-making and power, share news, pass on jokes, lampoon their leaders, provide information and so on is a defining characteristic of our species. Social media is the most obvious contemporary manifestation of this characteristic.

The recent power failure in South Australia showed the best and worst aspects of social media. It allowed people to communicate useful and important information quickly in the midst of the storm, but a political debate began almost immediately, and just as quickly devolved into binary positions. A complex issue was reduced to a slanging match, and the real issues were obscured.

Where to from here?

Social media is another form of communication that adds to the many we already have. How we adapt political debates and decision making to it is a work in progress.

One response would be a greater focus in education on logic, statistics and rhetoric to make social media communication more reliable, effective and hopefully, more civil.

For now, perhaps we could start with an algorithm to determine how many thousand posts on social media are equal to one conversation in the bar or coffee shop. Or develop a pearl of wisdom filter based on the quality of the message, and thereby boost national productivity by saving hours of time scrolling through 10,000 posts that essentially say the same two things.

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Article Contents

Defining social media, usage, and data, quality considerations for social media in research, current uses and evaluations of social media in research, legal and ethical considerations, the road ahead.

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Social Media in Public Opinion Research: Executive Summary of the Aapor Task Force on Emerging Technologies in Public Opinion Research

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Joe Murphy, Michael W. Link, Jennifer Hunter Childs, Casey Langer Tesfaye, Elizabeth Dean, Michael Stern, Josh Pasek, Jon Cohen, Mario Callegaro, Paul Harwood, Social Media in Public Opinion Research: Executive Summary of the Aapor Task Force on Emerging Technologies in Public Opinion Research, Public Opinion Quarterly , Volume 78, Issue 4, Winter 2014, Pages 788–794, https://doi.org/10.1093/poq/nfu053

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Public opinion research is entering a new era, one in which traditional survey research may play a less dominant role. The proliferation of new technologies, such as mobile devices and social-media platforms, is changing the societal landscape across which public opinion researchers operate. As these technologies expand, so does access to users’ thoughts, feelings, and actions expressed instantaneously, organically, and often publicly across the platforms they use. The ways in which people both access and share information about opinions, attitudes, and behaviors have gone through a greater transformation in the past decade than perhaps in any previous point in history, and this trend appears likely to continue. The ubiquity of social media and the opinions users express on social media provide researchers with new data-collection tools and alternative sources of qualitative and quantitative information to augment or, in some cases, provide alternatives to more traditional data-collection methods.

The reasons to consider social media in public opinion and survey research are no different than those of any alternative method. We are ultimately concerned with answering research questions, and this often requires the collection of data in one form or another. This may involve the analysis of data to obtain qualitative insights or quantitative estimates. The quality of data and the ability to help accurately answer research questions are of paramount concern. Other practical considerations include the cost efficiency of the method and the speed at which the data can be collected, analyzed, and disseminated. If the combination of data quality, cost efficiency, and timeliness required by a study can best be achieved through the use of social media, then there is reason to consider these methods for research.

An additional reason to consider social media in public opinion and survey research is its explosion in popularity over the past several years. At a time when many are eschewing landline telephones ( Blumberg and Luke 2013 ) or actively taking steps to prevent unsolicited contact (e.g., caller ID, restricted-access buildings), many are now communicating and interacting online via social-networking sites. It is only natural for researchers to aim to meet potential respondents where they have the best chance of getting their attention and potentially gaining their cooperation. However, this brave new world is not without its share of issues and pitfalls—technological, statistical, methodological, and ethical—and much remains to be investigated.

As the leading association of public opinion research professionals, AAPOR is uniquely situated to examine and assess the potential impact of new “emerging technologies” on the broader discipline and industry of opinion research. In September 2012, the AAPOR Council approved the formation of the Emerging Technologies Task Force with the goal of focusing on two critical areas: smartphones as data-collection vehicles and social media as platform and information source. The current report focuses on social media; a companion report covers mobile data collection.

This report examines the potential impact of social media on public opinion research—as a vehicle for facilitating some aspect of the survey research process (i.e., questionnaire development, recruitment, locating, etc.) and/or augmenting or replacing traditional survey research methods (i.e., content analysis of existing data). We distinguish between qualitative insights and quantitative indicators from social media and discuss the factors that must be evaluated to determine its fitness for use.

Social media has been defined in many ways, but for the purposes of this report, we borrow the definition from Murphy, Hill, and Dean (2013) , which is relevant for public opinion and survey research: “Social media is the collection of websites and web-based systems that allow for mass interaction, conversation, and sharing among members of a network.” Social-media platforms have proliferated in recent years, with a rapid increase in adoption and use by both members of the general public and specific subpopulations. Social media is not defined by a single type of platform or data. The list of popular platforms is long and can change rapidly. Platform types include blogs, microblogs, social-networking services, content-sharing and discussion sites, and virtual worlds.

According to the Pew Internet and American Life Project, as of 2013, 81 percent of the US adult population had Internet access, and of that population, 73 percent used social media. This rate differs most significantly by age group, but has increased dramatically over the past several years among all age groups. The largest demographic difference is by age. Social-networking sites are currently being used by nine in ten 18–29-year-olds but fewer than half of the 65+ population ( Duggan and Smith 2013 ). Although social-media popularity overall has skyrocketed in recent years, the popularity of individual social-media sites has risen and fallen over time. Certain other social-media platforms are highly popular outside the United States. And many platforms have changed in the features and access they offer over time.

Data from social-media platforms capture a variety of information and come in several different formats, with different access methods and levels of availability. Social-media data can be purely text based or include audio or visual components. Data from social-media sites can be accessed directly through the platform itself or through a range of partially to fully automated methods. The specific types of information available can also change rapidly in the social-media world. Platforms sometimes release large changes in both features and access with little to no warning.

A bounty of data is freely available to researchers, but the availability of data for research purposes is largely dependent on the terms and conditions of each site and is subject to change with little or no notice.

There are legitimate quality concerns with using social media in research. Not every member of the public uses these platforms, and those who do use them in different ways. In this respect, social media may provide useful insights for a particular set of questions, but perhaps not more specific point estimates that are generalizable to a broader population. The public nature of social media requires significant attention to be paid to the barriers for sharing honestly and openly. Just as we do with survey research, social media must be viewed practically and objectively and its potential advantages and error sources investigated and documented.

One of the most pervasive questions in using social media for data collection is its use for constructing a sample frame and recruiting respondents. To date, there has been little progress in attempts to show how data collected through the use of social-media sites can represent the general population. Because social-media users are not representative of the wider general public and due to the lack of reliable sampling frames, only nonprobability samples can currently be gathered in this way. Researchers must consider the universe of people who use the Internet, who uses social media among those on the Internet, and how those people are represented on social media.

Social-media research often faces issues of incomplete information. Unlike survey respondents, who typically provide information only when prompted, those who use social media tend to post what they want, when they want, prompted or not. Those using social media usually also continue to have the ability to control their content after it is posted. They can edit or remove posts or change the privacy settings related to those posts. Additionally, much of the content in social-media research frequently includes linked content across media sources, which are also subject to change. Along with the threat of incomplete data from social media, however, is the opportunity that comes from its abundance. Survey data are typically captured from individuals once in cross-sectional studies, and a limited number of times within particular time frames with longitudinal studies. Social media, on the other hand, allows for a more continuous look at opinion, attitudes, and behaviors, when shared and when reflecting the truth. The fact that social media is inherently public to some extent affects the likelihood that an individual will share the type of data in which we are interested. Stigma and social desirability may prevent honest and open sharing on certain topics.

In contrast to survey research, social-media researchers less frequently specify individual people as the unit of analysis. The units of analysis in social-media research can be individual posts, words, unique users, pages, or the like. Choosing a unit of analysis is usually a vitally important part of data selection and analysis.

Data analysis is done in a variety of ways by researchers from a wide variety of backgrounds. The most common form of data in social-media analysis is textual data, and textual data can be analyzed in many different ways. Text-mining algorithms are popular with a growing legion of data scientists, and sophisticated computer programs have been built by machine-learning experts to tackle the challenge of finding meaning in social media and other textual data. There is a growing set of text classifiers that are built by natural-language-processing specialists and linguists to uncover relevant underlying textual patterns using exploratory data visualizations, or markedly smaller-scale qualitative observations.

Researchers are recognizing the potential for social media to provide new options to conduct research more quickly, more efficiently, and in new ways than in the past. Many researchers have begun to explore and implement methods to incorporate social media in public opinion and survey research in two basic ways. The first is to actively identify, locate, or interact with study participants. The second is through passive monitoring, as an early warning or forecasting system, or as a supplement or alternative to survey data collection.

In the design phase of the survey life cycle, social media has been used to inform questionnaire design , allowing researchers new insights into the survey topics and populations under consideration. In testing and preparing for data collection, social media has been used for targeted recruitment of respondents for cognitive interviews and focus groups, using nonprobability sampling methods ( AAPOR 2013 ). In longitudinal studies, social media has been used to actively locate or stay in touch with sample members through outreach and engagement efforts. Finally, data from social media and web-based systems have been used as both a supplement and proxy for survey data by “scraping” websites for information on people’s self-reported characteristics, behaviors, opinions, and interests. We present and discuss examples of each type of use in the full report.

Because regulation of new technologies can be a slow process, assessment of all relevant legal regulations in this area of research is challenging. The lack of legal guidance specific to new technologies puts respondents potentially at risk and leaves researchers with unanswered questions. US regulations are applicable for research only within the United States. Other countries and political regions have different, and sometimes stricter, protections of human subjects and the preservation of data collected. In the absence of clear legal direction, researchers need to self-regulate, adapting survey screeners and research documentation to accommodate the portability and flexibility of the platform on which we wish to conduct research, so as to not erode the protection of human subjects. In addition to legal requirements pertaining to the locations of the researchers and participants, researchers must also adhere to the terms of use of the sites that they wish to use.

Though there is much debate on the topic of informed consent and passive social-media data collection, we argue that the way informed consent is applied depends on the public or private nature of the space. Public spaces can be likened to observing behavior in public. In situations where the terms of service clearly state that content will be made public, no consent should be necessary to conduct research on publicly available information. Researchers still need to maintain their code of ethics and protect the privacy of their research subjects. Researchers should also note the risks mentioned above with releasing information that can be reidentified. The benefits to the community should always outweigh the potential harm of doing research.

Though a good deal of research to date has focused on an array of issues related to social media, far less is known in terms of if, when, and how such data may be fit for use in public opinion and survey research. Currently, researchers are making use of social media to derive qualitative insights (rather than probability-based point estimates), for pretesting purposes, and for a recruiting resource for nonprobability surveys. Looking forward, it is necessary for researchers to continue investigation into social media’s ultimate utility for public opinion research and the extent to which it can serve as a resource for both qualitative and quantitative applications. This will require replicable, impartial, transparent experiments to gauge its effectiveness as a source of opinion, attitudes, and behaviors and/or as a platform for collecting such. Here, we highlight just a few of the priority areas of research for our field.

VALIDATING SOCIAL MEDIA

A question of paramount concern is whether social media, when used as a substantive source of data, can provide accurate answers to certain research questions. How do we know that posts on the Internet mean what we think they mean? Or if they were made by individuals at all (while the pace of ethnographic, behavioral, and linguistic research on social media is fast, many questions have yet to be answered about the growth of “bots” or computerized postings as well as those “paid to post”)? In order to provide some validation, we will need to interact with those who post on social media and learn more about their intentions, attitudes, and behaviors when producing content. Just as we have validated survey items against gold-standard data sources, we must also validate social media against more certain sources of information.

ADDRESSING COVERAGE, SAMPLING, AND DIFFERENTIAL ACCESS CHALLENGES

A second area of concern is whether social media can be representative of the general population or even a subset of Internet or social-media users. Although social-media research can accurately reflect activity online, more research is needed to determine whether we can create a frame of social-media users from which we can sample individuals for research with a known and non-zero probability. Research into inferred demographics is useful to fill in missing information on those who use social media, and detection of fake and duplicate accounts is also helping produce a clearer picture of the social-media landscape, but there is much work to be done to be certain whether and how social media may represent the real world or even a subset of that world. A related set of issues involves the differential access to and use of the Internet and social media across various subgroups of the population. The impact of differential access and use must be better understood and overcome if social media is to become a robust source of public opinion data in the years ahead.

DESIGNING BETTER INTEGRATIONS OF SURVEYS AND SOCIAL MEDIA

To date, few studies have been published that directly compare survey responses with online behaviors. But this is an appealing option, both because it may allow areas of survey coverage error to be explored in greater detail than with traditional survey research, and because it may allow social-media coverage to be explored in unprecedented ways through links to survey and administrative records.

LEVERAGING THE UNIQUE FEATURES OF SOCIAL MEDIA

Social-media research has many drawbacks when compared with survey data for the purpose of generalizable research. However, there are unique aspects of social media that make it ideally suited for other types of research. One major advantage of social media is that it can provide a glimpse into the social networks of individuals. Beyond social networks, there may be other unique features that become evident from social media and opportunities to investigate and supplement research with those that are fit for use.

CONTINUING TO REFINE UNDERSTANDING AND GUIDANCE ON PRIVACY AND ETHICS

As with other types of research, we must place paramount importance on questions related to the privacy and ethical implications of social-media research. Many questions remain to be answered about what topics are suitable for research with social media. We need a better understanding of the cases where benefits to the public of such research outweigh the possible harm. Balancing these privacy and ethical concerns along with the quality considerations and great potential for new insights into the study of public opinion, attitudes, and behaviors presents a significant challenge for the field of public opinion and survey research. It is incumbent upon the field to explore this new world in a way that holds true to our values of ethical research, impartiality, transparency, and maximizing accuracy and quality in our measurements.

AAPOR . 2013 . Report of the AAPOR Task Force on Non-Probability Sampling . Available at http://www.aapor.org/AM/Template.cfm?Section=Reports1&Template=/CM/ContentDisplay.cfm&ContentID=5963 .

Blumberg Stephen J. Luke Julian V . 2013 . Wireless Substitution: Early Release of Estimates from the National Health Interview Survey, July–December 2012. National Center for Health Statistics. Available at http://www.cdc.gov/nchs/data/nhis/earlyrelease/wireless201306.pdf .

Duggan Maeve Smith Aaron . 2013 . Social Media Update 2013. Pew Research Center . Available at http://www.pewinternet.org/files/old-media/Files/Reports/2013/Social%20Networking%202013_PDF.pdf .

Murphy Joe Hill Craig A. Dean Elizabeth . 2013 . “Social Media, Sociality, and Survey Research.” In Social Media, Sociality, and Survey Research , edited by Hill Craig A. Dean Elizabeth Murphy Joe , 1 – 34 . Hoboken, NJ : John Wiley and Sons .

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• Published: 26 July 2021

Large-scale quantitative evidence of media impact on public opinion toward China

• Junming Huang   ORCID: orcid.org/0000-0002-2532-4090 1 ,
• Gavin G. Cook 1 &
• Yu Xie 1 , 2  

Humanities and Social Sciences Communications volume  8 , Article number:  181 ( 2021 ) Cite this article

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• Cultural and media studies
• Politics and international relations

Do mass media influence people’s opinions of other countries? Using BERT, a deep neural network-based natural language processing model, this study analyzes a large corpus of 267,907 China-related articles published by The New York Times since 1970. The output from The New York Times is then compared to a longitudinal data set constructed from 101 cross-sectional surveys of the American public’s views on China, revealing that the reporting of The New York Times on China in one year explains 54% of the variance in American public opinion on China in the next. This result confirms hypothesized links between media and public opinion and helps shed light on how mass media can influence the public opinion of foreign countries.

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Introduction.

America and China are the world’s two largest economies, and they are currently locked in a tense rivalry. In a democratic system, public opinion shapes and constrains political action. How the American public views China thus affects relations between the two countries. Because few Americans have personally visited China, most Americans form their opinions of China and other foreign lands from media depictions. Our paper aims to explain how Americans form their attitudes on China with a case study of how The New York Times may shape public opinion. Our analysis is not causal, but it is informed by a causal understanding of how public opinion may flow from the media to the citizenry.

Scholars have adopted a number of wide-ranging and even contradictory approaches to explain the relationships between media and the American mind. One school of thought stresses that media exposure shapes public opinion (Baum and Potter, 2008 ; Iyengar and Kinder, 2010 ). Another set of approaches focuses on how the public might lead the media by analyzing how consumer demand shapes reporting. Newspapers may attract readers by biasing coverage of polarizing issues towards the ideological proclivities of their readership (Mullainathan and Shleifer, 2005 ), and with the advent of social media platforms such as Facebook and Twitter, traditional media are now more responsive to audience demand than ever before (Jacobs and Shapiro, 2011 ). On the other side of this equation, news consumers generally tend to seek out news sources with which they agree (Iyengar et al., 2008 ), and politically active individuals do so more proactively than the average person (Zaller, 1992 ).

Two other approaches address factors outside the media–public binary. The first, stresses the role of elites in opinion formation. While some, famously including Noam Chomsky, argue that news media are unwitting at best and at worst complicit “shills” of the American political establishment, political elites may affect public opinion directly by communicating with the public (Baum and Potter, 2008 ). Foreign elites may also influence American opinion because American reporters sometimes circumvent domestic sources and ask trusted foreign experts and officials for opinions (Hayes and Guardino, 2011 ). The second stresses how the macro-level phenomenon of public sentiment is shaped by micro-level and meso-level processes. An adult’s opinions on various topics emerge from their personal values, many of which are set during and around adolescence from factors outside of the realm of individual control (Hatemi and McDermott, 2016 ). Social networks may also affect attitude formation (Kertzer and Zeitzoff, 2017 ).

In light of these contradictory interpretations, it is difficult to be sure whether the media shape the attitudes of consumers or, on the other hand, whether consumers shape media (Baum and Potter, 2008 ). Moreover, most of the theories summarized above are tested on relatively small slices of data. In order to offer an alternative, “big data”-based contribution to this ongoing debate, this study compares how the public views China and how the news media report on China with large-scale data. Our data set, which straddles 50 years of newspaper reporting and survey data, is uniquely large and includes more than a quarter-million articles from The New York Times.

Most extant survey data indicate that Americans do not seem to like China very much (Xie and Jin, 2021 ). Many Americans are reported to harbor doubts about China’s record on human rights (Aldrich et al., 2015 ; Cao and Xu, 2015 ) and are anxious about China’s burgeoning economic, military, and strategic power (Gries and Crowson, 2010 ; Yang and Liu, 2012 ). They also think that the Chinese political system fails to serve the needs of the Chinese people (Aldrich et al., 2015 ). Most Americans, however, recognize a difference between the Chinese state, the Chinese people, and Chinese culture, and they view the latter two more favorably (Gries and Crowson, 2010 ). In Fiske’s Stereotype Content Model (Fiske et al., 2002 ), which expresses common stereotypes as a combination of “competence” and “warmth”, Asians belong to a set of “high-status, competitive out-groups” and rank high in competence but low in warmth (Lin et al., 2005 ).

The New York Times, which calls itself the “Newspaper of Record”, is the most influential newspaper in the USA and possibly even in the Anglophonic world. It boasts 7.5 million subscribers (Business Wire, 2021 ), and while the paper’s reach may be impressive, it is yet more significant that the readership of The New York Times represents an elite subset of the American public. Print subscribers to The New York Times have a median household income of $191,000, three times the median income of US households writ large (Rothbaum and Edwards, 2019 ). Despite the paper’s haughty and sometimes condescending reporting, it “has had and still has immense social, political, and economic influence on American and the world” (Schwarz, 2012 , p. 81). The New York Times may be a paper for America’s elite, and it may be biased to reflect the tastes of its elite audience, but the paper’s ideological slant does not affect our analyses as long as the its relevant biases are consistent over the time period covered by our analyses. Our analyses support the intuition of qualitative work on The Times (Schwarz, 2012 ) and show that these biases remain more or less constant for the decades in our sample. These analyses also illuminate some of the paper’s more notable biases, including the paper’s particular predilection for globalization.

The impact of social media on traditional media is not straightforward. While new media have certainly changed old media, neither has replaced the other. It is more accurate to say that old media have been integrated into new media and, in some ways, become a form of new media themselves. Twitter has accelerated the 2000s-era trends of information access that made it possible for news readers to find their own news and also enabled readers to interact with journalists (Jacobs and Shapiro, 2011 ), and the The New York Times seems to have made a significant commitment to the Twitter ecosystem. A quick glance at the follower count of The Times’ official Twitter account shows that it is one of the most influential accounts on the site, with almost 50 million followers. For comparison, both current president Joe Biden and vice president Kamala Harris have around 10 million followers. Most New York Times reporters additionally have “verified” accounts on the platform, which means that individual reporters may be incentivized to maintain public-facing profiles more now than in the past.

The media consumption patterns that made new media possible have changed the way The New York Times interacts with its audience and how it extracts revenue. The New York Times boasts a grand total of 7.5 million subscribers, but only 800,000 of them subscribe to the print edition. The Times’ digital subscription base has boomed since the election of Donald J. Trump, growing almost sixfold from a paltry 1.3 million in 2015 to a staggering 6.7 million in 2020 (Business Wire, 2021 ). The Times increasingly relies more on digital subscriptions and less on print subscriptions and ad sales for revenue (Lee, 2020 ). Ad revenue for most papers has been in sharp decline since the early 2000s (Jacobs and Shapiro, 2011 ), and this trend has only continued into the present. The New York Times now operates almost like a direct-to-consumer, subscription tech startup. New media have not replaced but have certainly changed old media. The full impact of these changes is beyond the scope of this paper, and we suggest it as an area for further research.

A small body of prior work has studied the The New York Times and how The New York Times reports on China. Blood and Phillips use autoregression methods on time series data to predict public opinion (Blood and Phillips, 1995 ). Wu et al. use a similar autoregression technique and find that public sentiment regarding the economy predicts economic performance and that people pay more attention to economic news during recessions (Wu et al., 2002 ). Peng finds that coverage of China in the paper has been consistently negative but increasingly frequent as China became an economic powerhouse (Peng, 2004 ). There is very little other scholarship that applies language processing methods to large corpora of articles from The New York Times or other leading papers. Atalay et al. is an exception that uses statistical techniques for parsing natural languages to analyze a corpus of newspaper articles from The New York Times, the Wall Street Journal, and other leading papers in order to investigate the increasing use of information technologies in newspaper classifieds (Atalay et al., 2018 ).

We explore the impact of The New York Times on its readers by examining the general relationship between The Times and public opinion. Though some might contend that only elites read NYT, we have adopted this research strategy for two reasons. If the views of NYT only impacted the nation’s elite, the paper’s views would still propagate to the general public through the elites themselves because elites can affect public opinion outside of media channels (Baum and Potter, 2008 ). Additionally, it is a widely held belief that NYT serves as a general barometer of an agenda-setting agent for American culture (Schwarz, 2012 ). Because of these two reasons, we interpolate the relationship between NYT and public opinion from the relationship between NYT and its readers, and we extrapolate that the views of NYT are broadly representative of American media.

Our paper aims to advance understanding of how Americans form their attitudes on China with a case study of how The New York Times may shape public opinion. We hypothesize that media coverage of foreign nations affects how Americans view the rest of the world. This reduced-form model deliberately simplifies the interactions between audience and media and sidesteps many active debates in political psychology and political communication. Analyzing a corpus of 267,907 articles on China from The New York Times, we quantify media sentiment with BERT, a state-of-the-art natural language processing model with deep neural networks, and segment sentiment into eight domain topics. We then use conventional statistical methods to link media sentiment to a longitudinal data set constructed from 101 cross-sectional surveys of the American public’s views on China. We find strong correlations between how The New York Times reports on China in one year and the views of the public on China in the next. The correlations agree with our hypothesis and imply a strong connection between media sentiment and public opinion.

We quantify media sentiment with a natural language model on a large-scale corpus of 267,907 articles on China from The New York Times published between 1970 and 2019. To explore sentiment from this corpus in greater detail, we map every article to a sentiment category (positive, negative, or neutral) in eight topics: ideology, government and administration, democracy, economic development, marketization, welfare and well-being, globalization, and culture.

We do this with a three-stage modeling procedure. First, two human coders annotate 873 randomly selected articles with a total of 18,598 paragraphs expressing either positive, negative, or neutral sentiment in each topic. We treat irrelevant articles as neutral sentiments. Secondly, we fine-tune a natural language processing model Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018 ) with the human-coded labels. The model uses a deep neural network with 12 layers. It accepts paragraphs (i.e., word sequences of no more than 128 words) as input and outputs a probability for each category. We end up with two binary classifiers for each topic for a grand total of 16 classifiers: an assignment classifier that determines whether a paragraph expresses sentiment in a given topic domain and a sentiment classifier that then distinguishes positive and negative sentiments in a paragraph classified as belonging to a given topic domain. Thirdly, we run the 16 trained classifiers on each paragraph in our corpus and assign category probabilities to every paragraph. We then use the probabilities of all the paragraphs in an article to determine the article’s overall sentiment category (i.e., positive, negative, or neutral) in every topic.

As demonstrated in Table 1 , the two classifiers are accurate at both the paragraph and article levels. The assignment classifier and the sentiment classifier reach classification accuracy of 89–96% and 73–90%, respectively, on paragraphs. The combined outcome of the classifiers, namely article sentiment, is accurate to 62–91% across the eight topics. For comparison, a random guess would reach an accuracy of 50% on each task (see Supplementary Information for details).

American public opinion towards China is a composite measure drawn from national surveys that ask respondents for their opinions on China. We collect 101 cross-sectional surveys from 1974 to 2019 that asked relevant questions about attitudes toward China and incorporate a probabilistic model to harmonize different survey series with different scales (e.g., 4 levels, 10 levels) into a single time series, capitalizing on “seaming” years in which different survey series overlapped (Wang et al., 2021 ). For every year, there is a single real value representing American sentiment on China relative to the level in 1974. Put another way, we use sentiment in 1974 as a baseline measure to normalize the rest of the time series. A positive value shows a more favorable attitude than that in 1974, and a negative value represents a less favorable attitude than that in 1974. Because of this, the trends in sentiment changes year-over-year are of interest, but the absolute values of sentiment in a given year are not. As shown in Fig. 1 , public opinion towards China has varied greatly from 1974 to 2019. It steadily climbed from a low of −24% in 1976 to a high of 73% in 1987, and has fluctuated between 10% and 48% in the intervening 30 years.

This time series is aggregated from 101 cross-sectional surveys from 1974 to 2019 that asked relevant questions about attitudes toward China with the year of 1974 as baseline. Years with attitudes above zero show a more favorable attitude than that in 1974, with a peak of 73% in 1987. Years with attitudes below zero show a less favorable attitude than that in 1974, with the lowest level of −24% in 1976. The time series is shown with a 95% confidence interval.

We begin with a demonstration of how the reporting of The New York Times on China changes over time, and we follow this with an analysis of how coverage of China might influence public opinion toward China.

Trend of media sentiment

The New York Times has maintained a steady interest in China over the years and has published at least 3,000 articles on China in every year of our corpus. Figure 2 displays the yearly volume of China-related articles from The New York Times on each of the eight topics since 1970. Articles on China increased sharply after 2000 and eventually reached a peak around 2010, almost doubling their volume from the 1970s. As the number of articles on China increased, the amount of attention paid to each of the eight topics diverged. Articles on government, democracy, globalization, and culture were consistently common while articles on ideology were consistently rare. In contrast, articles on China’s economy, marketization, and welfare were rare before 1990 but became increasingly common after 2000. The timing of this uptick coincided neatly with worldwide recognition of China’s precipitous economic ascent and specifically the beginnings of China’s talks to join the World Trade Organization.

In each year we report in each topic the number of positive and negative articles while ignoring neutral/irrelevant articles. The media have consistently high attention on reporting China government & administration, democracy, globalization, and culture. There are emerging interests on China’s economics, marketization, and welfare and well-being since 1990s. Note that the sum of the stacks does not equal to the total volume of articles about China, because each article may express sentiment in none or multiple topics.

While the proportion of articles in each given topic change over time, the sentiment of articles in each topic is remarkably consistent. Ignoring neutral articles, Figure 3 illustrates the yearly fractions of positive and negative articles about each of the eight topics. We find four topics (economics, globalization, culture, and marketization) are almost always covered positively while reporting on the other four topics (ideology, government & administration, democracy, and welfare & well-being) is overwhelmingly negative.

The panel reports the trend of yearly media attitude toward China in ( A ) ideology, ( B ) government & administration, ( C ) democracy, ( D ) economic development, ( E ) marketization, ( F ) welfare & well-being, ( G ) globalization, and ( H ) culture. The media attitude is measured as the percentages of positive articles and negative articles, respectively. US–China relation milestones are marked as gray dots. The New York Times express diverging but consistent attitudes in the eight domains, with negative articles consistently common in ideology, government, democracy, and welfare, and positive sentiments common in economic, globalization, and culture. Standard errors are too small to be visible (below 1.55% in all topics all years).

The NYT views China’s globalization in a very positive light. Almost 100% of the articles mentioning this topic are positive for all of the years in our sample. This reveals that The New York Times welcomes China’s openness to the world and, more broadly, may be particularly partial to globalization in general.

Similarly, economics, marketization, and culture are covered most commonly in positive tones that have only grown more glowing over time. Positive articles on these topics began in the 1970s with China–US Ping–Pong diplomacy, and eventually comprise 1/4 to 1/2 of articles on these three topics, the remainder of which are mostly neutral articles. This agrees with the intuition that most Americans like Chinese culture. The New York Times has been deeply enamored with Chinese cultural products ranging from Chinese art to Chinese food since the very beginning of our sample. Following China’s economic reforms, the number of positive articles and the proportion of positive articles relative to negative articles increases for both economics and marketization.

In contrast, welfare and well-being are covered in an almost exclusively negative light. About 1/4 of the articles on this topic are negative, and almost no articles on this topic are positive. Topics regarding politics are covered very negatively. Negative articles on ideology, government and administration, and democracy outnumber positive articles on these topics for all of the years in our sample. Though small fluctuations that coincided with ebbs in US–China relations are observed for those three topics, coverage has only grown more negative over time. Government and administration is the only negatively covered topic that does feature some positive articles. This reflects the qualitative understanding that The New York Times thinks that the Chinese state is an unpleasant but capable actor.

Despite the remarkable diversity of sentiment toward China across the eight topics, sentiment within each of the topics is startlingly consistent over time. This consistency attests to the incredible stability of American stereotypes towards China. If there is any trend to be found here, it is that the main direction of sentiment in each topic, positive or negative, has grown more prevalent since the 1970s. This is to say that reporting on China has become more polarized, which is reflective of broader trends of media polarization (Jacobs and Shapiro, 2011 ; Mullainathan and Shleifer, 2005 ).

Media sentiment affects public opinion

To reveal the connection between media sentiment and public opinion, we run a linear regression model (Eq. ( 1 )) to fit public opinion with media sentiment from current and preceding years.

where μ t denotes public opinion in year t with possible values ranging from −1 to 1. F k j s is the fraction of positive ( s  = positive) or negative ( s  = negative) articles on topic k in year j . Coefficient β k j s quantifies the importance of F k j s in predicting μ t .

There is inertia to public opinion. A broadly held opinion is hard to change in the short term, and it may require a while for media sentiment to affect how the public views a given issue. For this reason, j is allowed to take [ t , t  − 1, t  − 2, ...] anywhere from zero to a couple of years ahead of t . In other words, we inspect lagged values of media sentiment as candidate predictors for public attitudes towards China.

We seek an optimal solution of media sentiment predictors to explain the largest fraction of variance ( r 2 ) of public opinion. To reduce the risk of overfitting, we first constrain the coefficients to be non-negative after reverse-coding negative sentiment variables, which means we assume that positive articles have either no impact or positive impact and that negative articles have either zero or negative impact on public opinion. Secondly, we require that the solution be sparse and contain no more than one non-zero coefficient in each topic:

where r 2 ( μ , β , F ) is the explained variance of μ fitted with ( β , F ). The l 0 -norm ∥ β k , ⋅ , ⋅ ∥ 0 gives the number of non-zero coefficients of topic k predictors.

The solution varies with the number of topics included in the fitting model. As shown in Table 2 , if we allow fitting with only one topic, we find that sentiment on Chinese culture has the most explanatory power, accounting for 31.2% of the variance in public opinion. We run a greedy strategy to add additional topics that yield the greatest increase in explanatory power, resulting in eight nested models (Table 2 ). The explanatory power of our models increases monotonically with the number of allowed topics but reaches a saturation point at which the marginal increase in variance explained per topics decreases after only two topics are introduced (see Table 2 ). To strike a balance between simplicity and explanatory power, we use the top two predictors, which are the positive sentiment of culture and the negative sentiment of democracy in the previous year, to build a linear predictor of public opinion that can be written as

where F culture, t −1,positive is the yearly fraction of positive articles on Chinese culture in year t  − 1 and F democracy, t −1,negative is the yearly fraction of negative articles on Chinese democracy in year t  − 1. This formula explains 53.9% of the variance of public opinion in the time series. For example, in 1993 53.9% of the articles on culture had a positive sentiment, and 46.9% of the articles on democracy had negative sentiment ( F c u l t u r e ,1993,positive  = 0.539, F democracy,1993,negative  = −0.469). Substituting those numbers into Eq. ( 2 ) predicts public opinion in the next year (1994) to be 0.208, very close to the actual level of public opinion (0.218) (Fig. 4 ).

The public opinion (solid), as a time series, is well fitted by the media sentiments on two selected topics, namely “Culture” and “Democracy”, in the previous year. The dashed line shows a linear prediction based on the fractions of positive articles on “Culture” and negative articles on “Democracy” in the previous year. The public opinion is shown with a 95% confidence interval, and the fitted line is shown with one standard error.

By analyzing a corpus of 267,907 articles from The New York Times with BERT, a state-of-the-art natural language processing model, we identify major shifts in media sentiment towards China across eight topic domains over 50 years and find that media sentiment leads public opinion. Our results show that the reporting of The New York Times on culture and democracy in one year explains 53.9% of the variation in public opinion on China in the next. The conclusion that we draw from our results is that media sentiment on China predicts public opinion on China. Our analysis is neither conclusive nor causal, but it is suggestive. Our results are best interpreted as a “reduced-form” description of the overall relationship between media sentiment and public opinion towards China.

While there are a number of potential factors that may complicate our conclusions, none would change the overall thrust of our results. We do not consider how the micro-level or meso-level intermediary processes through which opinion from elite media percolates to the masses below may affect our results. We also do not consider the potential ramifications of elites communing directly with the public, of major events in US–China relations causing short-term shifts in reporting, or of social media creating new channels for the diffusion of opinion. Finally, The New York Times might have a particular bias to how it covers China.

In addition to those specified above, a number of possible extensions of our work remain ripe targets for further research. Though a fully causal model of our text analysis pipeline may prove elusive (Egami et al., 2018 ), future work may use randomized vignettes to further our understanding of the causal effects of media exposure on attitudes towards China. Secondly, our modeling framework is deliberately simplified. The state affects news coverage before the news ever makes its way to the citizenry. It is plausible that multiple state-level actors may bypass the media and alter public opinion directly and to different ends. For example, the actions and opinions of individual high-profile US politicians may attenuate or exaggerate the impact of state-level tension on public sentiment toward China. There are presumably a whole host of intermediary processes through which opinion from elite media affects the sentiment of the masses. Thirdly, the relationship between the sentiment of The New York Times and public opinion may be very different for hot-button social issues of first-line importance in the American culture wars. In our corpus, The New York Times has covered globalization almost entirely positively, but the 2016 election of President Donald J. Trump suggests that many Americans do not share the zeal of The Times for international commerce. We also plan to extend our measure of media sentiment to include text from other newspapers. The Guardian, a similarly elite, Anglophonic, and left-leaning paper, will make for a useful comparison case. Finally, our analysis was launched in the midst of heightened tensions between the US and China and concluded right before the outbreak of a global pandemic. Many things have changed since COVID-19. Returning to our analysis with an additional year or two of data will almost certainly provide new results of additional interest.

Future work will address some of these additional paths, but none of these elements affects the basic conclusion of this work. We find that reporting on China in one year predicts public opinion in the next. This is true for more than fifty years in our sample, and while knowledge of, for example, the opinion diffusion process on social media may add detail to this relationship, the basic flow of opinion from media to the public will not change. Regarding the putative biases of The New York Times, its ideological slant does not affect our explanation of trends in public opinion of China as long as the paper’s relevant biases are relatively consistent over the time period covered by our analyses.

Data availability

All data analyzed during the current study are publicly available. The New York Times data were accessed using official online APIs ( https://developer.nytimes.com/ ). We used their query API to search for 267,907 articles that mention China, Chinese, Beijing, Peking, or Shanghai. We downloaded the full text and date of each article. The survey data were obtained from three large public archives/centers, namely Roper Center for Public Opinion Research (ROPER), NORC at the University of Chicago, and Pew Research Center (Pew Research Center, 2019 ; Smith et al., 2018 ). See Supplementary Information for a full list of surveys. The source codes and pretrained parameters of the natural language processing model BERT are publicly released by Google Inc. on its github repository ( https://github.com/google-research/bert ). The finetuned BERT models and the inferred sentiment of The New York Times articles in our corpus are publicly available at Princeton University DataSpace. Please check the project webpage ( http://www.attitudetowardchina.com/media-opinion ) or the DataSpace webpage ( https://doi.org/10.34770/x27d-0545 ) to download.

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Acknowledgements

The authors thank Chesley Chan (Princeton University), Wen Liu (Renmin University of China), Yichun Yang (Renmin University of China), and Emily Yin (Princeton University) for coding The New York Times articles with the topic-specific sentiment. The authors thank Chih-Jou Jay Chen (Academia Sinica), Cheng Cheng (Singapore Management University), Shawn Dorius (Iowa State University), Theodore P. Gerber (University of Wisconsin-Madison), Fengming Lu (Australian National University), Yongai Jin (Renmin University of China), Donghui Wang (Princeton University) for valuable discussions. The authors thank Xudong Guo (Tsinghua University) for helping fine-tune the BERT model and analyze the calculation results. The authors thank Tom Marling for proofreading the manuscript. The research was partially supported by the Paul and Marcia Wythes Center on Contemporary China at Princeton University and Guanghua School of Management at Peking University.

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Huang, J., Cook, G.G. & Xie, Y. Large-scale quantitative evidence of media impact on public opinion toward China. Humanit Soc Sci Commun 8 , 181 (2021). https://doi.org/10.1057/s41599-021-00846-2

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The impact of social media on public opinion

After the impact of social media on public opinion has proved itself to be the most substantial impact a form of media can have on an audience, social media has been used as a powerful tool by politicians, marketers, brands, companies, and even individuals to achieve their goals.

Social media usage in public opinion has many objectives that differ from one case to another, as we will explain in more detail in this article. However, they can be summed up in two general objectives:

1.     Changing an unwanted situation by creating a collective mindset through which the public can unite to take a stand on social or political issues and create social movements.

2.     Creating a positive mental image for the brand or company in the public’s mind.

Introduction on how social media influences public opinion

After the technological revolution dominated the world and changed our lifestyle and way of exchanging knowledge, social media became the most significant influence on public opinion.

A person’s awareness, personality, and thinking pattern are shaped by what they acquire from the behaviors and information present in their surrounding environment. That environment includes; family, friends, school, places of worship, religious practices, and any other place or means they can interact with others and learn from. That can happen through face-to-face communication or any cultural or artistic forms such as; books, music, or movies.  

With the advent of the internet and social media, they have become a major component of the environment that constitutes and affects human awareness, inclinations, opinions, and even behaviors. It has also become the easiest way to communicate with different people from all over the world, access information from various sources in the easiest and fastest way, and learn the diverse ideologies of different cultures. That is how social media influences public opinion and moves the audience towards the desired behavior.

What impact does social media have on society exactly, may you ask?

The role of social media in public opinion has been profound and evident since it started gaining attention and attracting interest years ago. In many countries, including Egypt, especially after the 2011 revolution, the understanding of the importance of social media has been increasing each day.

As mentioned by Lamia Kamel in CC Plus, the new generation recognized that social media platforms are not just made for entertainment; they also have a crucial role in helping the uprise of social movements, leading them to achieve their goals and changing political views. That forced people with authority to also believe in the power of the double-edged sword that is social media.

Social media: the new replacement for public platforms

Social networks are tools used to reach and influence the public sphere. A public sphere consists of a group of citizens who gather and discuss different matters. That can happen through various platforms, including; cafes, public places, clubs, etc. 

However, with the advent of social media, it has become an invaluable addition if not the perfect replacement for the traditional platforms.

How does social media influence social movements? 

Social movements were always an effective weapon that people used on a national and international level all around the world to restructure the past, reshape the present, and change the future of society.

Through social media, communication among the public sphere has been a lot easier. Also, considering the billions of people browsing the internet and including social media in their lifestyle, those social movements became able to reach larger audiences. That changed the idea behind a public sphere, as it wasn’t exclusive to the citizens of a specific country. Now, social movements can reach everyone through Facebook, Twitter, or any other platform, making social movements an international matter in which everyone can participate.

How can social media enforce social movements?

1.     The rapid spread of information helps to validate and expand the movement.

2.     Helping social movements propagate a narrative through which they can gain empathy.

3.     Enable the protestors to bypass censorship forced by the government in some cases.

4.     Create hashtags and trends to voice their demands, and force the authorities to take actions to their advantage in order to avoid public rage.

5.     Prevent the danger of physical presence, which was the main characteristic of social movements in the past.

6.     Simplify the process of organizing social movements’ activities as the traditional social movements were restricted by limited mobility across physical spaces. 

7.     Changing the dynamics of old social movements to help everyone speak themselves.

In the older dynamic, social movements required opinion leaders, which made it hard to listen to the lower voices within the crowd. Nowadays, everyone is a leader of their own movement, and chances are provided more equally.

  Most inspirational social media brand campaigns in response to social issues:

1. “ For Once, Just Don’t Do It “ – Nike:

Video: https://twitter.co m/Nike/status/1266502116463370241  

Nike was not a stranger to the racism movements in America. As a matter of fact, it has always shown its support to such issues. The “For Once, Just Don’t Do It” Campaign was made in response to the murder of George Floyd due to police brutality. 

The Campaign involved statements rolled out on social media channels. Nike said: “For once, don’t do it. Don’t pretend there’s not a problem in America. Don’t turn your back on racism”.

 The campaign gained a lot of approval from the public, including its own rival Adidas retweeting the message. As mentioned by Forbes , Nike’s PR team set an example of the importance of responding to social issues and taking turns to change the situation for the better.

2. “ We Accept “ – Airbnb:

Video: https://www.youtube.com/watch?v=yetFk7QoSck  

  The popular home-sharing site launched a politically charged campaign under the hashtag (#WeAccept) . The campaign took a stand against the previous American president, Donald Trump’s nation-dividing travel ban.

The campaign drew positive national attention from the public, especially after reading the announcement, which stated: “We believe no matter who you are, where you’re from, who you love or who you worship, we all belong. The world is more beautiful the more you accept”. 

3. “ Project Body Hair “ – Billie:

Although Billie is a razor selling brand, it launched a campaign in 2019 under the hashtag #ProjectBodyHair . The campaign wanted to raise awareness about female beauty standards by pointing out that women’s razor brands do not show body hair, although it exists.

As a great tactic to raise brand awareness through social media platforms, the brand encourages women through their website to use the hashtag and increase their library of women on the internet with body hair.

The campaign contributes to the different social movements throughout the years regarding the issue of positive body image and stereotypes. 

According to a survey done by Sprout Social , around 66% of consumers said they believed brands should weigh in on social issues because they can create real change. At the same time, 63% reaffirmed that brands have platforms to reach a large audience. And in another survey done by them, almost half of the consumers wanted brands to take those stands on social media.

That can only prove that the understanding of the consumers and audience’s needs, reaching out on social media as their most desired and used channel of communication, in addition to a good PR team, can majorly affect the public opinion and the image of the brand.  

How social media affects political views

As we discussed previously, social media has been a significant factor in the uprise of many social movements. It was used as a channel for many brands to launch their own campaigns as a stand on social issues.

However, social media was also able to affect the political views which the public adopted. Not only that but also the thought process through which they reach these conclusions.

According to Virginia Tech expert Mike Horning, when false information spreads across social media about the electoral process being unfair, this could impact how the public feels about the entire election process and undermine their belief in democracy.

 As we have seen ourselves for the past few years, elections around the world have lost their reputation among the public. This is where the role of PR agencies comes, as they are able to translate the situation honestly and positively to the public. 

PR agencies with expertise in the political field can choose the appropriate approach and launch campaigns through social media to educate the public on the electoral process and reach a wider audience. In case you were looking for political PR services, the most recommended ones in Egypt would be CC Plus’s stakeholder management services that apply specific industry knowledge with government authorities that deliver winning results.

Examples of social media political movements 

1. Iranian Green Movement:

We have stated that the public views on the elections have been affected negatively. The controversy surrounding the Iranian elections led to Iranians’ protests against the highly controversial Iranian presidential elections in 2009.

2. Arab Spring:

Arab Spring is a series of anti-government protests and uprisings as a response to oppressive regimes and a low standard of living. It started with Tunisia and spread across the Arab world, mainly: Egypt, Libya, Yemen, Syria, and Bahrain, all with the help of social media platforms.

3. The Egyptian Revolution of 2011:

“Bread, freedom, and social justice!”

This was the chant that took over Tahrir Square in Egypt. Millions of Egyptian youth decided that on the day of 25 January 2011, they’d leave a mark in the history of social revolutions, a revolution in which Facebook – one of the most influential social media platforms – was its true leader. It was proof that the impact of social media on public opinion is impeccable.

A Facebook page was created by the activist Wael Ghoniem in the name of “We are all Khalid Said,” a young man killed as a result of police brutality. The page encouraged Egyptians to take a stand against the mistreatment of the police and reject the oppression practiced by the authorities on the citizens. It also demanded the overthrow of the former Egyptian president Hosni Mubarak.

(Video) CNN: Egyptian activist, Wael Ghonim ‘Facebook to thank for freedom’: https://www.youtube.com/watch?v=JS4-d_Edius  

Traditional Media: What should be said, and what should not?

Traditional media used to have a remarkable power on public opinion. Radios, Televisions, and Newspapers surrounded us everywhere as we grew up. They shaped our minds and thoughts.

Fast enough, the government took over those channels. Nowadays, people suffer the same issue with the traditional media as the electoral process, which is dishonesty. 

Traditional media needed the official channels to broadcast its message and influence the audience. This forced the media to adopt the ideas of the government and the upper hands, which made its message insincere and fake.

The impact of media coverage on public opinion

In addition to that, social media provided people with a two-ways-communication system. Not only are they exposed to different ideas and opinions, but they are also able to express themselves through discussions within their virtual communities and give feedback to the content creators.

However, with the clever media monitoring provided by PR agencies, their clients can make accurate decisions at the right time. This can help create a healthier relationship between the public and the content creators, whether that is local or international newspapers, radio/TV broadcasts, newsletters, and the internet. 

What’s the general purpose of how social media influences public opinion?

The objective of using social media as a channel to influence public opinion varies from one situation to another. The article at hand showed many cases in which social media can be used to make a change through the powerful effect it has on people.

In some of these cases, the purpose was to change the political situation completely; The Arab Spring and The Egyptian Revolution of 2011 are good examples. In others, just like the case with Airbnb, Nike, and Billie, the purpose of such campaigns was to support persecuted minorities, challenge social norms, and voice the public’s thoughts to gain their approval and support in return.

Conclusion on how social media influences public opinion.

Although social media proved to be a tool of freedom in the hands of its users, it started to show its negatives recently. False information, cyberbullying, and invasion of users’ privacy became the main problems users suffer from on a daily basis. Social media platforms owners are still trying to execute their policies more strictly to prevent those issues from happening.

On the other hand, the traditional media had its impact on public opinion in the past. When social media came along, with all its advantages and possibilities, it replaced the traditional press, proving to have a massive effect on public opinion.

With the world progressing each day and experts trying to find more accessible and faster ways to communicate, it can be expected that a new channel of communication may replace social media in the future or be able to influence public opinion even more powerfully. 

How can PR agencies help influence public opinion?

PR agencies understand the impact of social media on public opinion as they have an essential role in helping the process. As a result, CC Plus provides multiple services through which any company/brand/or even independent creators can create a positive image for themselves and gain the trust and approval from the audience. This can be achieved through Corporate Social Responsibility services and Cause Marketing.

These Agencies’ expertise in Government Relations and Political PR is also needed to help with the governmental issues related to public opinions, such as the voters’ behaviors and views on the political processes.

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The Power of Media in Shaping Public Opinion

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Historical overview of media influence on public opinion, the role of media in influencing public opinion, case studies of media influence on public opinion, positive and negative effects of media influence on public opinion, enhancing media responsibility in influencing public opinion, a. agenda setting, d. persuasion, a. positive effects, b. negative effects, c. ethical considerations.

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I. INTRODUCTION

Ii. empirical analysis of opinion formation on online social media, iii. the model, iv. simulation results, a. statistical characteristics of user behavior and opinion update, b. dependence of opinion evolution on parameters, v. conclusions, acknowledgments, appendix: data processing, opinion formation on social media: an empirical approach.

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Fei Xiong , Yun Liu; Opinion formation on social media: An empirical approach. Chaos 1 March 2014; 24 (1): 013130. https://doi.org/10.1063/1.4866011

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Opinion exchange models aim to describe the process of public opinion formation, seeking to uncover the intrinsic mechanism in social systems; however, the model results are seldom empirically justified using large-scale actual data. Online social media provide an abundance of data on opinion interaction, but the question of whether opinion models are suitable for characterizing opinion formation on social media still requires exploration. We collect a large amount of user interaction information from an actual social network, i.e., Twitter, and analyze the dynamic sentiments of users about different topics to investigate realistic opinion evolution. We find two nontrivial results from these data. First, public opinion often evolves to an ordered state in which one opinion predominates, but not to complete consensus. Second, agents are reluctant to change their opinions, and the distribution of the number of individual opinion changes follows a power law. Then, we suggest a model in which agents take external actions to express their internal opinions according to their activity. Conversely, individual actions can influence the activity and opinions of neighbors. The probability that an agent changes its opinion depends nonlinearly on the fraction of opponents who have taken an action. Simulation results show user action patterns and the evolution of public opinion in the model coincide with the empirical data. For different nonlinear parameters, the system may approach different regimes. A large decay in individual activity slows down the dynamics, but causes more ordering in the system.

Opinion dynamics tries to describe the process of public opinion formation in social systems. Many opinion models have been presented that explore how the local individual behavior affects collective phenomena. However, those model results are seldom empirically justified using large-scale actual data, and whether traditional opinion models suitably describe online opinion interactions requires further exploration. We analyze users' opinions regarding a certain topic using large-scale actual data collected from a famous social network, i.e., Twitter, and discover two nontrivial results: first, consensus is difficult to achieve in a finite time and second, users seldom change their opinions, and the number of individual opinion changes decays as a power law. We present a discrete opinion model including agents' internal opinions and external actions that are determined by agents' activity. Agents' activity also evolves during the dynamics. Simulation results show our model can retrieve similar properties to those of actual data. We hope theoretical opinion models will be verified by actual data in different social systems so that they can better characterize actual social interactions. In the future, whether opinion models can predict the evolutionary trend of public opinion in actual situation will be investigated. This study will improve the applicability of research on opinion dynamics.

In recent years, many opinion models have been presented, simulating opinion interactions from a personal perspective. Given an individual interacting rule, the research on opinion dynamics aims to understand the global complex properties in the area of social science. 1,2 Statistical methods are used to explore how the local rules affect the collective behavior of social agents. 3 In these models, agents hold one of several possible opinions, corresponding to discrete opinion models, 4–7 or the opinions of agents take value from a certain range of real numbers, i.e., continuous opinion models. 8,9 Starting from an initial opinion configuration, agents update their opinions according to the interacting rules, and finally the models try to find the formation of public opinion and the conditions of phase transition. Discrete opinion models, such as the Ising model and the Sznajd model, 10–12 tend to use the analogy of ferromagnetic spins from the science of solid-state physics. Interacting rules are defined in opinion models, and agents update their states following the pattern of ferromagnetic spins, explaining social systems through the analogy of physical systems. In most models, neighboring influence plays a vital role in individual decisions. The final macroscopic state of the system may be consensus, fragmentation, or polarization. In binary opinion models, the consensus state is often favored as a result of imitation and compromises among neighbors, but polarization may also be observed in some other discrete models, such as the model with discrete vectorial opinions 13 and the voter model with three opinions. 14 In addition, virtual and actual complex networks have been used to mediate opinion interaction. 15–17 Sociological and psychological features have also been introduced into opinion models, such as memory, 18 inertia, 19,20 noise, 21 and conviction, 22 characterizing the way in which these features change individual behavior and the global dynamics in a specific scenario.

Although more and more actual factors are being included in opinion models, the question of whether the models can adequately describe the process of opinion formation in real society and explain or even predict social phenomena still requires further exploration. In Ref. 23 , the Finland 2003 election data of the voting sets were applied to verify an opinion model, and it was found that the transient opinion profiles produced by the model are in agreement with the real data. In Ref. 24 , the authors studied political discussions on an Internet forum. They chose several hundred posts and identified their sentiments. Focusing on the growth and topology of the network, they proved that quarrels and personal conflicts between participants boost the growth of discussions. They identified the final state of discussions, finding that opinion exchanges do not lead to consensus formation and opinions tend to go to extremes. Similar results are found in Ref. 25 . These studies 24,25 raise the question of whether traditional opinion models suitably describe online opinion interactions, and whether the interacting rules reflect the actual characteristics of human behavior. Except for these few studies, due to the limitations of data acquisition and processing capability, large-scale empirical analysis has seldom been carried out to check the validity of opinion models.

The Internet has become one of the most important ways to obtain information. As a popular application service on the Internet, online social media have attracted millions of users. On social media, users interact with others, build relationships, publish posts or replies, and discuss topics. Therefore, the growth of social networks is promoted by users' actions. The process of opinion formation on social media is more complicated than in real society, and information diffuses and evolves more rapidly. For instance, users always discuss issues with others anonymously. They do not know the true names of their neighbors, and they cannot become well acquainted with the personality characteristics of their neighbors. Moreover, users cannot directly see the internal opinions of their neighbors but instead learn about their opinions through the posts they publish. In Refs. 26 and 27 , a model with continuous opinions and discrete actions was presented; the findings suggested that after observing the actions of neighbors, agents update their own internal opinions that cannot be noticed by others. This model may be an attempt to interpret the opinion evolution on social media.

Social media provide huge amounts of user and topic information. In Refs. 28 and 29 , the authors empirically studied the spread of health behavior in an online social network. In Refs. 30 and 31 , the dynamics of health behavior sentiments was investigated by real data collected from a large social network. From social media, one can also collect user relationships and posts easily and freely, and analyze the sentiments of the posts to reproduce the opinion evolution process. In this study, we collect abundant data from a popular social medium, i.e., Twitter, and obtain users' opinions from posts. Studying the evolution of public opinion, we determine differences in opinion formation compared with traditional opinion models. Based on our findings, we propose a discrete opinion model in which individual opinions are latent and only agents' actions are accessible to others. Individual actions are induced by their activity, which evolves during the opinion evolution, and individual opinion changes nonlinearly depending on the proportion of opponents.

The rest of the paper is structured as follows. Section II carries out an empirical analysis of opinion interaction on Twitter. Section III presents a model with internal opinions and external actions driven by individual activity. Section IV illustrates simulation results and provides a discussion about the model. Concluding remarks are given in Sec. V .

Twitter, a microblogging service, has become one of the most popular social media on the Internet. With its huge number of readers, Twitter has demonstrated its strength for information propagation. Twitter is sometimes the source of popular topics and can even cause online emergencies. Therefore, studying public opinion on Twitter can help us to understand the opinion formation process in online social networks.

We collected an abundance of data from Twitter through our directed robot, including information about users and their related posts. After several hours of collection from Twitter, 2 348 854 user profiles and approximately 6 million posts from December 2010 to June 2011 were downloaded. The data in each month contain 301 184, 908 976, 967 328, 1 390 116, 1 282 112, 1 354 921, and 725 435 posts, respectively, and contain 164 154, 435 632, 447 910, 611 082, 586 182, 592 893, and 435 813 active users, respectively. Users of Twitter usually publish a post to express their ideas, attitudes, or sentiments toward a social event or product. User's posts do not always track the real evolution process of user's opinions because a user may not publish posts all the time, and the active users that create posts frequently only comprise a small proportion of the population. Even so, we can also analyze the total posts belonging to a certain topic to investigate the dynamic trend of public opinion on Twitter. In our sentiment analysis (see the Appendix), these posts may have positive or negative polarity. In this paper, we treat the sentiment of a post as user's current opinion so users can hold one of two possible opinions on a topic. We chose three topics about electronic products (i.e., “iPhone 4,” “Blackberry,” “iPad 2”) and gathered related posts. Each of the topics contains 102 815, 225 954, and 199 702 posts, respectively. For the three topics, we calculated the proportion of cumulative positive posts at different time to quantify the opinion dynamics on Twitter; thus, we can observe the change of public opinion over time.

In this paper, we consider that the system achieves an ordered state, if there exists a clear majority-minority splitting of two opinions, and the polarization of opinions can also be observed; however, in a disordered state, the densities of the two opinions are equal and no opinion dominates. 32 Figure 1 shows the evolution of the proportion of total positive posts on these topics. As shown in Figure 1 , the public opinion for each topic fluctuates in the early stage, and then the dynamics is extremely slowed down after a short time. Ultimately, the public opinion evolves into an ordered state and one opinion predominates absolutely, but a state of complete consensus is difficult to reach. At first, one opinion takes a slight advantage, but this advantage gradually grows toward the initial majority opinion in the evolution process. On these three topics, the majority of people hold a commendatory attitude, and more approvers are attracted by the product “iPhone 4” and “iPad 2.” We also study the evolution of other topics, and find that an ordered state is a common stable state for online dynamic systems. In Ref. 33 , the authors proposed a non-consensus opinion model in which agents within a community hold the same opinion but opinions of agents across communities are different. Then, we check the real user network of Twitter, and cannot find a clear community structure with consensus within, and the proportion of positive posts for these three topics in any subset of the real network is generally different.

Time evolution for the proportion of total positive posts on three topics.

Now, we study the participation level of users in each topic. As shown in Figure 2 , the distribution of the number of users' posts decays as a power law with a long tail. The power exponents of the distribution for “iPhone 4,” “iPad 2,” and “Blackberry” are γ = − 2.343   ±   0.008 ⁠ , γ = − 2.451   ±   0.004 ⁠ , and γ = − 2.767   ±   0.011 ⁠ , respectively. More than 10 000 users only publish one post on a certain topic, but several enthusiasts discuss and comment on the product more than 100 times. Meanwhile, the heterogeneity in users' participation in the topic “Blackberry” remains the largest among these topics, implying that users have less interest in this topic. It is also found that the results for the topics “iPhone 4” and “iPad 2” are similar. The reason may be that these two products “iPhone 4” and “iPad 2” are provided by the same corporation, and a lot of users hold positive attitude towards the corporation, as well as its products. Many users participate in both the topics “iPhone 4” and “iPad 2,” and have similar activity on these two topics.

Distribution for the number of users' posts on the three topics. The slopes of the straight lines for “iPhone 4,” “iPad 2,” and “Blackberry” are −2.343, −2.451, and −2.767, respectively.

Figure 3 shows the distribution for the number of individual opinion changes in the interaction. Agents' internal opinions are recorded according to the posts published by them, so we can only consider those agents that publish posts at least twice to track opinion changes. As shown in Figure 3 , although users create many posts to express their opinions, the influence of these posts on the opinions of neighbors is not effective, and users tend to insist on their original attitude about a product. We calculate the data, and find although people are less active on the topic “Blackberry,” more users tend to update opinions. One reason may be that some users do not form a deep impression on the topic until they withdraw from the interaction. It is also found that the distribution for the number of opinion changes approximately follows a power law. The power exponents for “iPhone 4,” “iPad 2,” and “Blackberry” are γ = − 2.193   ±   0.143 ⁠ , γ = − 3.01   ±   0.116 ⁠ , and γ = − 2 . 68   ±   0.174 ⁠ , respectively. One may think that the distribution of individual opinion changes is caused by the power-law decay of the number of posts. However, as in Figure 4 , the number of individual opinion changes does not have a clear relationship with the number of posts.

Distribution for the number of individual opinion changes. The slopes of the straight line for “iPhone 4,” “iPad 2,” and “Blackberry” are −2.193, −3.01, and −2.68, respectively.

The number of individual opinion changes versus the number of posts for each agent.

In the empirical analysis of the Twitter data, we found that users of social media prefer to keep their original opinions. The complete consensus is difficult to reach, and the system often evolves into an ordered state. In fact, even if agents can update their internal opinions, they do not always publish posts to share their ideas with the public and may drop out of interaction. In consideration of the characteristics of online social interaction, we present the following opinion model.

We assume that each agent has two properties, its opinion and activity. Agents hold one of two possible opinions, a positive σ = + 1 or a negative opinion σ = − 1 ⁠ . It should be noted that agents' opinions are latent, and that agents are unaware of the internal opinions of their neighbors. If an agent takes an action (e.g., publishes a post on social media) to express its opinion at time t ⁠ , then its neighbors can notice its choice. The opinion of this agent's action is the same as its opinion at time t ⁠ . This means that at the following time, the agent may change its opinion, but if it does not take a further action, neighbors can only realize its opinion at time t ⁠ . However, the agent's current opinion may no longer be consistent with that action. An agent's decision depends on its activity, which is denoted by τ ⁠ , and the most active agent has a higher priority to take an action. Agent's activity is influenced by its neighbors. If an agent expresses its opinion, the neighbors see its action and are motivated, so that their activity on the topic increases.

Now, we will introduce the interacting rule of our model. In the beginning, all agents are frozen, i.e., their activity τ = 0 ⁠ . Then, m agents are selected at random, and their activity is increased by 1. These agents are initially active agents that start a conversation on a topic. At each time step, m agents with the highest activity are selected to express their opinions. Any agent that has ever taken an action is considered to be active and attentive to the topic, and thus has the opportunity to update its opinion. Then, agents' states are changed as follows:

After agents take an action, their activity decays by the proportion δ ( ⁠ δ < 1 ⁠ ). Agents have no reason to maintain their activity, and are unwilling to repeatedly publish the same opinion.

Agents' actions provide a demonstration for their neighbors, and neighbors are likely to be motivated because agents usually imitate other's behavior. Therefore, after each agent's action, the activity of its neighbors increases by 1.

After noticing the recent actions, neighbors are aware of the opinions of these agents. In addition to their activity, active neighbors will also update their opinions. The probability that active neighbors will change their opinions nonlinearly depends on the fraction of disagreeing active agents in their local environment, similar to Refs. 34 and 35 .

For instance, if agent i publishes its opinion at time t ⁠ , then its activity τ i decreases by δ τ i and all of its neighbors increase their activity by 1. Let j denote one of agent i 's active neighbors, and then agent j will update its opinion. Assuming the fraction of agent j 's neighbors taking an opposite action with j is p ⁠ , the probability of agent j changing its opinion is defined as p α ( ⁠ α > 0 ⁠ ). It should be mentioned that agent j can only see the external actions of its neighbors, ignoring the inactive agents that have not take any action. This means that although some agents may disagree with agent j , if these agents do not express their idea to the public, their opinion has no influence on j .

From the above model, when α = 1 ⁠ , one recovers the voter model, and the average magnetization is conserved. When α → 0 ⁠ , agents change their opinions with nearly the same probability and more randomly. Therefore, the initial discrepancy between the density of two opinions will decrease gradually, and the model will evolve almost like a coin-flip. When α > 1 ⁠ , agents change opinions only when a large fraction of neighbors disagree, and the prevalence of initial majority opinion is enhanced. However, if α is too large, the opinion dynamics will become frozen and the average magnetization remains unchanged. Obviously, in our model, the probability that an agent with fully disagreeing neighbors changes its opinion is equivalent to 1, despite the parameters.

We consider a simplified case, assuming that agents freely update their opinions on a fully connected network, regardless of the activity. We conduct an analysis of the overall opinion. The overall density of opinion +1 at time t is denoted by f ( t ) ⁠ . Thus, the evolution of f ( t ) is shown as follows:

Especially, when α = 1 ⁠ , the variation of f ( t ) equals zero, thus leading to f ( ∞ ) = f ( 0 ) ⁠ . For α ≠ 1 ⁠ , it is easy to obtain the solution f ( ∞ ) (simply written as f ⁠ ) to Eq. (1) ; that is, f = 0 ⁠ , f = 1 ⁠ , or f = 0.5 ⁠ . We can analyze the stability of these stationary points by introducing small deviations around these solutions. 19 When α < 1 ⁠ , the stable solution is f = 0.5 ⁠ . When α > 1 ⁠ , the stable solution is f = 1 or f = 0 ⁠ , depending on the initial condition. If f ( 0 ) > 0.5 ⁠ , then f = 1 ⁠ , and vice versa. However, in the model, agents take an action according to their activity, and the withdrawal from the interaction for agents will prevent the complete consensus state.

We are interested in the co-evolution of individual opinions and activity. We will investigate the heterogeneity of the participation level of agents, and how likely an agent will be to change its opinion following those of its neighbors in our model. In the analytical approach, we neglect agents' activity, so that for a larger α ⁠ , the system achieves the consensus state. Now, we will consider the influence of individual activity on its external actions and study the process of opinion evolution. First, we will use a real social network as an interaction topology, and obtain the statistical characteristics of agents' activity and opinion distribution. Then, we will construct a virtual network that mediates the dynamics of the model, studying the relationship between the final opinion distribution and the parameters, and how the nonlinear probability of opinion change takes effect.

In the simulations, agents' initial opinions are assigned uniformly with a given ratio between the two opinions. After m agents with the highest activity take an action and their active neighbors update their opinions asynchronously, the time step is increased by 1. Therefore, the parameter m determines the overall time scale, which should not be too large.

We downloaded node information and relationships from Twitter to build a real network. In the network, all neighboring relationships were downloaded, so the network could be treated as a subset of Twitter. The network contains 4286 nodes, and the average degree of the network is 29.38. The network is used to mediate interactions in our model. Here, we concentrate on the macroscopic distribution of agents' opinions and activity.

Figure 5 shows the temporary evolution of the proportion of total positive actions from our model. We choose three initial opinion configurations f ( 0 ) = 0.4 ⁠ , f ( 0 ) = 0.6 ⁠ , and f ( 0 ) = 0.8 ⁠ . Obviously, the proportion of action +1 has a drastic change in the early stage, and after a short time, it tends to level off towards the direction of consensus. The density difference between the two opinions is enhanced because more agents turn to the initial majority opinion. The magnetization is not conserved, and the system becomes more ordered. Moreover, if the decay parameter δ is too large, the dynamics stabilize more slowly, but the consensus state is still hard to reach within a finite time. In Figure 5 , for a large initial density of f ( 0 ) = 0.8 ⁠ , a few agents still take the negative opinion and are difficult to persuade even with more time. These phenomena are in accordance with the opinion evolution of real topics. In addition, although some agents with low activity may drop out of the interaction, the dynamic process is not stopped. Active agents continue to publish their opinions, but these two opinions reach a state of balance. Although the system still evolves extremely slowly, approaching the ordered absorbing state, agents' activity-based behavior prevents the occurrence of complete consensus in a finite time.

Time evolution for the proportion of total positive actions with different initial opinion assignments, δ = 0.2 ⁠ , m = 20 ⁠ , and α = 2 ⁠ .

As illustrated in Figure 6 , the distribution of individual participation level on the real network in our model has almost a power-law scaling, similar to the actual data in Figure 2 . Intuitively, with a large activity decay, agents will become increasingly reluctant to take actions. Thus, as the decay parameter δ increases, the number of agents that seldom publish their own opinions also increases, leading to a larger absolute value of the power exponent. In addition, when the number of actions is above 30, the corresponding number of agents is scattered in a large range, and does not strictly adhere to the power-law scaling. We also find that, when the parameter m is small, the distribution of individual participation level is almost independent of m ⁠ . However, if m becomes extremely large, especially when it approaches the system size although this is unrealistic, the power law does not exist. In the model, agents with a larger degree are motivated more often and thus have large activity. Therefore, the underlying network still has influence on individual actions. We investigate the network extracted from the social medium, and find that the node degree has a Poisson-like distribution. Furthermore, we also implement simulations on large-scale small-world and scale-free networks with the same average degree k ¯ = 30 and achieve analogous results in Figure 7 . The average degree of the network does not affect the existence of power-law distribution, but the absolute value of the power exponent decreases with the increase in the average degree.

Distribution of the number of individual actions with various values of δ ⁠ , f ( 0 ) = 0.6 ⁠ , m = 20 ⁠ , and α = 2 ⁠ . The slopes of the straight lines for δ = 0.8 ⁠ , δ = 0.6 ⁠ , and δ = 0.2 are −2.2017, −1.675, and −1.246, respectively.

Distribution of the number of individual actions in different networks, N = 50000 ⁠ , m = 20 ⁠ , δ = 0.8 ⁠ , and α = 2 ⁠ . The slope of the straight line is −2.6142.

We explore the frequency of individual opinion changes in our model. Consistent with Figure 3 , opinion changes are recorded according to external actions because only external actions can be observed by the public; i.e., agents that publish their opinions at least twice are taken into consideration. In Figure 8 , no extremeness is introduced into our discrete model, but agents driven by activity rarely change their ideas, coinciding with the actual situation in Figure 3 . When α = 1 ⁠ , the model reduces to the voter dynamics, but agents do not have continual opinion interactions as they do in the voter model due to the influence of individual activity. Increasing the nonlinear parameter α ⁠ , only agents with a large fraction of opponents will reconsider the other opinion, so agents tend to keep their original opinions. We realize that even if agents only update opinions a few times, most agents will eventually adopt the majority opinion.

Distribution of the number of individual opinion changes, f ( 0 ) = 0.6 ⁠ , m = 20 ⁠ , and δ = 0.8 ⁠ . The slopes of the straight lines for α = 1 ⁠ , α = 1.5 ⁠ , and α = 2 are −2.011, −2.36, and −3.1769, respectively.

The proportion of action +1 does not always reflect the evolutionary process of internal opinions because some active agents may take more actions and express their opinions more frequently. Therefore, we investigate the detailed internal opinion dynamics with different initial conditions, and calculate the final opinion distribution. Scale-free networks are used as an interacting topology with an average degree of 20. The number of agents N is 10 000.

The order parameter 36 measures the disordering of a system and characterizes the density divergence of two opinions. A larger order parameter means that the majority opinion accounts for a larger proportion of the population. If the order parameter equals 0, the two opinions have the same density. The order parameter is defined as

where ⟨ ... ⟩ denotes the configurational average. Figure 9 illustrates the final order parameter in our model with different initial opinion assignments. The opinion evolution process is intrinsically determined by the nonlinear parameter α ⁠ , and is also related to f ( 0 ) ⁠ . For α = 1 ⁠ , the order parameter changes linearly with f ( 0 ) ⁠ , indicating the conserved magnetization; however, consensus cannot be reached in any realization. The order parameter remains unchanged during the evolution, and this result coincides with the mean-field analysis. When α < 1 ⁠ , the order parameter goes down, implying that the majority opinion has less advantages. Agents perhaps change their opinions even when faced by a small fraction of opponents, making the opinion distribution more random. In particular, a much smaller α will reduce the difference of order parameters with different values of f ( 0 ) ⁠ . However, as a result of the influence of agents' activity, the disordered state with zero magnetization is hardly achieved. For α > 1 ⁠ , the system evolves into a more ordered state. The density of the initial majority opinion is enlarged, leading to a large order parameter. The system tends to stabilize quickly, and then the dynamics becomes much slower, so that it becomes difficult for individual opinions to converge. We also find that for α ≤ 1 ⁠ , if f ( 0 ) is set around 0.5, then the final densities of these two opinions are approximately equal to each other; however, for α > 1 ⁠ , one opinion will still predominate because of random fluctuations. In addition, larger m promotes individual opinion interaction and increases the variation of the order parameter during the dynamics, but the parameter m cannot affect the final essential regime of the system.

Final order parameter as a function of f ( 0 ) with different values of α after 1000 time steps, δ = 0.2 ⁠ . In the left plot, m = 20 ⁠ , and in the right plot, m = 40 ⁠ . The results are averaged over 100 different realizations.

Figure 10 shows the final order parameter with different activity decays. Clearly, the activity decay δ does not change the essential ordering of the system, but it can influence the relaxation process. The order parameter is always larger than that of the conserved system. Dramatically, a large decay δ slows the dynamics and decelerates individual interactions but gives the system a larger order parameter. The reason is that when the agents that take actions lose their activity more rapidly, other agents have an additional chance to express their own opinions. Therefore, the extent of interactions is adequately broadened, and more agents will reconsider and update their opinions, thereby strengthening the advantage of the majority opinion. In particular, for δ = 0.8 ⁠ , even if f ( 0 ) is approximately 0.5, a large order parameter is produced.

Final order parameter with different values of δ after 1000 time steps, m = 20 ⁠ , and α = 3 ⁠ . The plot is an average of 100 different simulations.

To investigate the actual opinion formation process and to verify the validity of opinion models for actual interactions especially on social media, we obtained an abundance of data from Twitter. We collected more than one million posts, and assessed their sentiments because posts created by users reflect their internal opinions. Then, we analyzed the user behavior patterns and the opinion formation process. We found that the public opinion often evolves into an ordered state in which one opinion dominates absolutely in the population. Moreover, we found that agents would rather express their opinions than change them. We proposed an opinion model in which agents have discrete internal opinions and external actions. Agents take actions according to their activity, and that activity evolves during the dynamics. Agents' actions have the same sentiment as their internal opinions, and can influence their neighbors, whose opinions depend on the proportion of neighboring opponents. We carried out computer simulations for our model, and compared the results with empirical studies.

Results show that the distribution of the number of individual actions decays as a power law in different underlying networks and that agents tend to maintain their original opinions. These phenomena agree with the actual situation on social media. With dissimilar nonlinear parameter of opinion update, the system evolves into distinctly different regimes. If the parameter equals 1, then the average magnetization is conserved. If the parameter is above 1, the system achieves a more ordered state where one opinion takes the majority, in agreement with actual opinion formation in social networks. However, when the parameter is below 1, the ordering of the system will decrease. The decay of individual activity decelerates opinion interactions, but large activity decay may lead to more ordering of individual opinions.

Although the sentiment classification algorithm we used has excellent performance for short-text documents, the accuracy of our results can still be improved by implementing new algorithms of sentiment analysis in the future. Moreover, in this paper, users' internal opinions are gained from the posts published by them; however, some users may not always publish posts to express their opinions, even if their opinions have changed. We cannot track the real-time opinion evolution of the agents that are not active enough. Therefore, our results are just treated as a large-scale sample of the real social network. An interesting Twitter application that can attract users to express their feelings towards a given topic should be developed, and the popularity of the application determines the reasonableness of the data collected by it. Furthermore, in the future, we hope to build a realistic model capable of predicting the evolutionary trend of public opinion and to use actual data to train parameters, instead of presetting parameters at the beginning of simulations in our current model.

This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61172072 and 61271308, the Beijing Natural Science Foundation under Grant No. 4112045, the Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20100009110002, the Beijing Science and Technology Program under Grant No. Z121100000312024, the Fundamental Research Funds for the Central Universities under Grant No. 2014JBM018. We also thank Dr. Pang Wu for his help of processing data.

Most of the posts downloaded from Twitter were written in English, and therefore, we only concentrated on English posts. The language detection tools provided by Cybozu Labs 37 were used to eliminate the posts in other languages. This tool divides documents into different language categories based on the Naive Bayes classifier, and the accuracy of the tool approaches 99%. Next, the algorithm of lexicon-based constrained symmetric nonnegative matrix factorization (CSNMF) was used to implement the sentiment classification of users' posts. 38 CSNMF is suitable for sentiment analysis of short texts on online social media and can achieve a high accuracy. 39 CSNMF was evaluated by labeled short-text documents, and performed very well. 39 Moreover, from the global view with a large data set, the errors of sentiment analysis in a single post tend to cancel out, 40 so that the average sentiment of total posts is reliable. After analyzing the sentiment values of all posts, we deleted the descriptive posts that do not contain users' attitudes, preferences, or emotions, and only conserved the posts that have either positive or negative polarity.

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The Power of Media: How It Shapes Public Opinion and Society

by Danny Ballan | Apr 7, 2023 | General Spotlights , Social Spotlights

• ·         Agenda Setting
• ·         Selective Reporting
• ·         Framing
• ·         Opinion Leaders
• ·         Social Media

The Impact of Media on Society

The role of the media in shaping public opinion.

From the time we wake up to the time we go to bed, we are constantly bombarded with news from the media, whether it be through television, newspapers, or social media. As a result, the media plays a crucial role in shaping our opinions on various topics, from politics to entertainment and beyond.

In this article, we will explore the ways in which the media influences public opinion and the impact it has on society as a whole.

· Agenda Setting

One of the most significant ways the media shapes public opinion is through agenda-setting. This is the process by which the media decides which topics and issues to cover and how they are presented to the public. Essentially, the media has the power to frame the conversation around certain topics, which can ultimately influence how people think about them.

For example, during election seasons, the media may focus heavily on certain candidates or issues, leading the public to form opinions based on what they see and hear. In this way, the media has the power to sway public opinion in favor of one candidate or political party over another.

· Selective Reporting

Another way the media can shape public opinion is through selective reporting. This is the process by which the media chooses which stories to report on and which to ignore. By selectively reporting on certain stories and ignoring others, the media can influence how the public perceives a particular issue.

For example, if the media only reports on negative news about a particular group or organization, the public may develop a negative perception of that group or organization, even if there is positive news that is not being reported. In this way, the media has the power to shape public opinion by selectively reporting on certain stories.

· Framing

Framing is the way in which the media presents information to the public. By framing information in a certain way, the media can influence how people interpret it. For example, if the media presents a story about a politician in a negative light, the public may view that politician negatively, even if there are positive aspects to their character or policy positions.

On the other hand, if the media presents a story in a positive light, the public may view the subject of the story in a more positive light. In this way, the media has the power to frame the conversation around certain topics, which can ultimately influence how people think about them.

· Opinion Leaders

Opinion leaders are individuals or groups that have a significant impact on public opinion. The media can play a role in shaping public opinion by highlighting the opinions of opinion leaders. For example, if a celebrity or influential public figure expresses an opinion on a particular topic, the media may give that opinion more weight and influence how the public thinks about the issue.

· Social Media

Social media has become an increasingly powerful tool for shaping public opinion. Through platforms like Twitter and Facebook, individuals can share their opinions and perspectives with a global audience. Social media also allows for the rapid dissemination of information, making it easier for individuals and groups to influence public opinion on a particular issue.

However, social media also has its downsides. With the rise of fake news and misinformation, it can be difficult to discern what is true and what is not. This can lead to the spread of false information and conspiracy theories , which can ultimately shape public opinion in dangerous ways.

The media’s influence on public opinion can have a significant impact on society. For example, media coverage of police shootings and racial inequality has led to a growing awareness of these issues and a call for change. Similarly, media coverage of environmental issues has led to increased awareness and action to address climate change .

However, the media can also have a negative impact on society . For example, sensationalized reporting can lead to a distorted perception of reality , leading to fear, anxiety, and even panic among the public. This can be particularly problematic in times of crisis, such as natural disasters, terrorist attacks, or pandemics, where the media’s coverage can exacerbate the situation and cause harm to individuals and communities.

Moreover, the media’s influence on public opinion can also have political implications. In democracies, the media plays a crucial role in shaping public opinion and influencing political outcomes. Media coverage of political campaigns, for example, can affect voters’ perceptions of candidates and their positions, ultimately influencing the election results.

However, the media’s influence on politics can also be problematic, particularly when it comes to issues of bias and partisanship. In recent years, the media has been criticized for promoting certain political agendas or ideologies and for perpetuating a polarized political environment.

The media plays a crucial role in shaping public opinion on a wide range of issues, from politics to social and environmental issues. Through agenda setting, selective reporting, framing, opinion leaders, and social media, the media has the power to influence how people think and feel about certain topics.

However, the media’s influence on public opinion is not without its drawbacks. Sensationalized reporting, fake news, and biased reporting can distort reality and perpetuate negative stereotypes and misconceptions. Moreover, the media’s influence on politics can be problematic, particularly when it comes to issues of bias and partisanship.

As media consumers, it is important to be aware of the media’s influence on our opinions and to critically evaluate the information we receive. By doing so, we can ensure that our opinions are based on accurate information and that we are not being unduly influenced by media bias or sensationalism.

• Media : Forms of communication that reach large audiences, such as television, newspapers, and the internet.
• Public Opinion : Views held by a group of people about a particular issue, product, or person.
• Society : A group of individuals living in a particular geographic area who share a common culture and institutions.
• Agenda Setting : The process by which the media decides which topics and issues to cover and how they are presented to the public.
• Selective Reporting : The process by which the media chooses which stories to report on and which to ignore.
• Framing : The way in which the media presents information to the public.
• Opinion Leaders : Individuals or groups that have a significant impact on public opinion.
• Social Media : Online platforms that allow users to create, share, and exchange information and ideas with others.
• Misinformation : False or inaccurate information that is spread unintentionally or deliberately.
• Fake News : Deliberately misleading or fabricated news stories.
• Sensationalized Reporting : Reporting that exaggerates or sensationalizes the facts in order to attract attention or increase viewership.
• Bias : Prejudice or favoritism towards a particular group or ideology.
• Partisanship : Strong support for a particular political party or ideology.
• Polarization : The process by which individuals and groups become more ideologically divided and less willing to compromise.
• Democracy : A system of government in which power is held by the people, usually through elected representatives.
• Natural Disasters : Events such as hurricanes, earthquakes, and floods that are caused by natural forces.
• Terrorism : The use of violence and intimidation in the pursuit of political aims.
• Pandemics : An outbreak of a disease that spreads across a large geographic area and affects a large number of people.
• Environmental Issues : Problems related to the natural world, such as climate change , pollution, and habitat destruction.

Election Results : The outcome of an election, usually determined by the number of votes cast for each candidate or political party.

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Social networks have too much political influence, Americans tell researchers

More than three-quarters of American adults—78%, up from 72% four years ago—think social media companies have too much power and influence in politics, the latest Pew Research study shows.

Break it down by political affiliation, and the research gets really interesting. Those leaning Republican have a longer track record of thinking social media companies have too much political influence, with 82% saying as much in 2020, and 84% in February of this year. But the numbers have really shot up for those leaning Democrat—74% this year, up from 63% four years ago.

Pew doesn’t get into reasons here, but I can hazard a couple guesses. Firstly, the latest survey came in the context of the Israel-Gaza war, in which Meta in particular has been accused of censoring pro-Palestinian views. Secondly and perhaps more obviously, Elon Musk. The tycoon took over Twitter with a promise to rectify what he saw as a leftist bias, then went on to regularly amplify right-wing—and in some cases far-right—voices, creating a situation where X (as it is now called) feels biased in that direction. Many left-ish users have since decamped to rival platforms like Threads and Bluesky.

Perhaps there’s even been some impact here from the furor around TikTok and its alleged use by Beijing to influence U.S. opinion—traditionally Republicans have been more hawkish on China, but the outrage has become very much bipartisan in recent months.

Pew’s research suggests that people generally see social media as a malign phenomenon in 2024—64% of respondents said it had a mostly negative effect on U.S. politics, with just 10% opting for mostly positive.

Those are the same overall figures as in 2020, but again, break it down by affiliation and it’s clear that Democrats are now markedly more likely to see the negative side of social media (59% up from 53%) but Republicans are actually decreasingly likely to see it (71% down from 78%,) even though a whopping 93% of Republican respondents believe social media companies are probably censoring political views that the companies disagree with. (A relatively mild 74% of Dems share this belief, though that’s considerably up from 66% in 2020.)

I’m interested to know your thoughts on these findings— drop me an email —but my read is that there’s a political realignment taking place on U.S. social media, with left-leaning users now less happy with the situation than they were several years ago. Not that either side is particularly thrilled with how things are panning out, mind you.

More news below, and do check out my colleague Alexandra Sternlicht’s latest piece on the TikTok national-security controversy—TikTok’s claims about keeping Americans’ data out of Beijing’s hands are looking decidedly shaky. And do also keep an eye out for Amazon’s results later today, which should make it clearer how profitable —or not—the generative AI boom is proving at this early stage.

David Meyer

Want to send thoughts or suggestions to Data Sheet? Drop a line  here .

Meta misinformation probe. The European Commission is probing Meta over its allegedly weak response to Russian disinformation campaigns on its services. As The Verge reports , the formal investigation under the EU’s new Digital Services Act (which governs content on big online platforms) is also looking into the efficacy of Meta’s mechanism for letting users flag illegal content, and its decision to demote political content in people’s feeds. Meanwhile, the Washington Post reports that Meta’s Oversight Board—independent, but funded by the company—is cutting jobs as a cost-saving measure.

More Tesla layoffs. Tesla is laying off even more staff, after its recent 10% headcount reduction. This round includes the whole Supercharger team—which rolls out Tesla’s chargers and has been successful in getting many other EV manufacturers to adopt its charging standard—as well as many of Tesla’s public policy workers.

Carriers fined over location data. The Federal Communications Commission has fined T-Mobile, Sprint, AT&T, and Verizon nearly $200 million in total for illegally selling their customers’ location data to aggregators that then resold the data to “bail-bond companies, bounty hunters, and other shady actors,” FCC Chair Jessica Rosenworcel said, according to Reuters .

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“We’re moving full steam ahead to bring Fortnite and the Epic Games Store to iPhones in the EU soon and iPads this year!”

— Epic Games reacts with delight to the European Commission’s designation yesterday of iPadOS as a “gatekeeper” under the new Digital Markets Act, which means Apple must allow third-party app stores on its tablets as well as its already-designated iPhones.

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BEFORE YOU GO

Apple’s secretive Swiss lab.  Apple has built up a “significant outpost” in Zurich to host much of its AI work, the Financial Times reports . It’s apparently called the Vision Lab. The report also notes that Apple has poached at least three dozen AI and machine learning workers from rival Google.

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Opinion: Does social media rewire kids’ brains? Here’s what the science really says

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America’s young people face a mental health crisis, and adults constantly debate how much to blame phones and social media. A new round of conversation has been spurred by Jonathan Haidt’s book “The Anxious Generation,” which contends that rising mental health issues in children and adolescents are the result of social media replacing key experiences during formative years of brain development.

The book has been criticized by academics , and rightfully so. Haidt’s argument is based largely on research showing that adolescent mental health has declined since 2010, coinciding roughly with mass adoption of the smartphone. But of course, correlation is not causation. The research we have to date suggests that the effects of phones and social media on adolescent mental health are probably much more nuanced.

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That complex picture is less likely to get attention than Haidt’s claims because it doesn’t play as much into parental fears. After all, seeing kids absorbed in their phones, and hearing that their brains are being “rewired,” calls to mind an alien world-domination plot straight from a sci-fi film.

And that’s part of the problem with the “rewiring the brain” narrative of screen time. It reflects a larger trope in public discussion that wields brain science as a scare tactic without yielding much real insight.

First, let’s consider what the research has shown so far . Meta-analyses of the links between mental health and social media give inconclusive or relatively minor results. The largest U.S. study on childhood brain development to date did not find significant relationships between the development of brain function and digital media use . This month, an American Psychological Assn. health advisory reported that the current state of research shows “ using social media is not inherently beneficial or harmful to young people” and that its effects depend on “pre-existing strengths or vulnerabilities, and the contexts in which they grow up.”

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So why the insistence from Haidt and others that smartphones dangerously rewire the brain? It stems from misunderstandings of research that I have encountered frequently as a neuroscientist studying emotional development, behavioral addictions and people’s reactions to media.

Imaging studies in neuroscience typically compare some feature of the brain between two groups: one that does not do a specific behavior (or does it less frequently) and one that does the behavior more frequently. When we find a relationship, all it means is either that the behavior influences something about the functioning of this brain feature, or something about this feature influences whether we engage in the behavior.

In other words, an association between increased brain activity and using social media could mean that social media activates the identified pathways, or people who already have increased activity in those pathways tend to be drawn to social media, or both.

Fearmongering happens when the mere association between an activity such as social media use and a brain pathway is taken as a sign of something harmful on its own. Functional and structural research on the brain cannot give enough information to objectively identify increases or decreases in neural activity, or in a brain region’s thickness, as “good” or “bad.” There is no default healthy status quo that everybody’s brains are measured against, and doing nearly any activity involves many parts of the brain.

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“The Anxious Generation” neglects these subtleties when, for example, it discusses a brain system known as the default mode network. This system decreases in activity when we engage with spirituality, meditation and related endeavors, and Haidt uses this fact to claim that social media is “not healthy for any of us” because studies suggest that it by contrast increases activity in the same network.

But the default mode network is just a set of brain regions that tend to be involved in internally focused thinking, such as contemplating your past or making a moral judgment, versus externally focused thinking such as playing chess or driving an unfamiliar route. Its increased activity does not automatically mean something unhealthy.

This type of brain-related scare tactic is not new. A common version, which is also deployed for smartphones , involves pathways in the brain linked to drug addiction, including areas that respond to dopamine and opioids. The trope says that any activity associated with such pathways is addictive, like drugs, whether it’s Oreos , cheese , God , credit card purchases , sun tanning or looking at a pretty face . These things do involve neural pathways related to motivated behavior — but that does not mean they damage our brains or should be equated with drugs.

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Adolescence is a time when the brain is particularly plastic, or prone to change. But change doesn’t have to be bad. We should take advantage of plasticity to help teach kids healthy ways to self-manage their own use of, and feelings surrounding, smartphones.

Do I expect future findings on the adolescent brain to immediately quell parents’ fears on this issue? Of course not — and the point is that they shouldn’t. Brain imaging data is a fascinating way to explore interactions between psychology, neuroscience and social factors. It’s just not a tool for declaring behaviors to be pathological. Feel free to question whether social media is good for kids — but don’t misuse neuroscience to do so.

Anthony Vaccaro is a postdoctoral research associate at the University of Southern California’s Psychology department.

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Numbers, Facts and Trends Shaping Your World

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• Public Attitudes Toward Computer Algorithms
• 2. Algorithms in action: The content people see on social media

Table of Contents

• 1. Attitudes toward algorithmic decision-making
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• The American Trends Panel Survey Methodology

The social media environment is another prominent example of algorithmic decision-making in Americans’ daily lives. Nearly all the content people see on social media is chosen not by human editors but rather by computer programs using massive quantities of data about each user to deliver content that he or she might find relevant or engaging. This has led to widespread concerns that these sites are promoting content that is attention-grabbing but ultimately harmful to users – such as misinformation, sensationalism or “hate clicks.”

To more broadly understand public attitudes toward algorithms in this context, the survey asked respondents a series of questions about the content they see on social media, the emotions that content arouses, and their overall comfort level with these sites using their data to serve them different types of information. And like the questions around the impact of algorithms discussed in the preceding chapter, this portion of the survey led with a broad question about whether the public thinks social media reflects overall public sentiment.

On this score, a majority of Americans (74%) think the content people post on social media does not provide an accurate picture of how society feels about important issues, while one-quarter say it does. Certain groups of Americans are more likely than others to think that social media paints an accurate picture of society writ large. Notably, blacks (37%) and Hispanics (35%) are more likely than whites (20%) to say this is the case. And the same is true of younger adults compared with their elders: 35% of 18- to 29-year-olds think that social media paints an accurate portrait of society, but that share drops to 19% among those ages 65 and older. Still, despite these differences, a majority of Americans across a wide range of demographic groups feel that social media is not representative of public opinion more broadly.

Social media users frequently encounter content that sparks feelings of amusement but also see material that angers them

When asked about six different emotions that they might experience due to the content they see on social media, the largest share of users (88% in total) say they see content on these sites that makes them feel amused. Amusement is also the emotion that the largest share of users (44%) frequently experience on these sites.

Social media also leads many users to feel anger. A total of 71% of social media users report encountering content that makes them angry, and one-quarter see this type of content frequently. Similar shares say they encounter content that makes them feel connected (71%) or inspired (69%). Meanwhile, around half (49%) say they encounter content that makes them feel depressed, and 31% indicate that they at least sometimes see content that makes them feel lonely.

Identical shares of users across a range of age groups say they frequently encounter content on social media that makes them feel angry. But other emotions exhibit more variation based on age. Notably, younger adults are more likely than older adults to say they frequently encounter content on social media that makes them feel lonely. Some 15% of social media users ages 18 to 29 say this, compared with 7% of those ages 30 to 49 and just 4% of those 50 and older. Conversely, a relatively small share of older adults are frequently amused by content they see on social media. In fact, similar shares of social media users ages 65 and older say they frequently see content on these platforms that makes them feel amused (30%) and angry (24%).

A recent Pew Research Center analysis of congressional Facebook pages found that the “anger” emoticon is now the most common reaction to posts by members of Congress. And although this survey did not ask about the specific types of content that might make people angry, it does find a modest correlation between the frequency with which users see content that makes them angry and their overall political affiliation. Some 31% of conservative Republicans say they frequently feel angry due to things they see on social media (compared with 19% of moderate or liberal Republicans), as do 27% of liberal Democrats (compared with 19% of moderate or conservative Democrats).

Social media users frequently encounter people being overly dramatic or starting arguments before waiting for all the facts to emerge

Along with asking about the emotions social media platforms inspire in users, the survey also included a series of questions about how often social media users encounter certain types of behaviors and content. These findings indicate that users see two types of content especially frequently: posts that are overly dramatic or exaggerated (58% of users say they see this type of content frequently) and people making accusations or starting arguments without waiting until they have all the facts (59% see this frequently).

A majority of social media users also say they at least sometimes encounter posts that appear to be about one thing but turn out to be about something else, as well as posts that teach them something useful they hadn’t known before. But in each instance, fewer than half say they see these sorts of posts frequently.

Beyond the emotions they feel while browsing social media, users are exposed to a mix of positive and negative behaviors from others. Around half (54%) of social media users say they typically see an equal mix of people being kind or supportive and people being mean or bullying. Around one-in-five (21%) say they more often see people being kind and supportive on these sites, while a comparable share (24%) says they more often see people being mean or bullying.

Previous surveys by the Center have found that men are slightly more likely than women to encounter any sort of harassing or abusive behavior online. And in this instance, a slightly larger share of men (29%) than women (19%) say they more often see people being mean or bullying content on social media platforms than see kind behavior. Women, on the other hand, are slightly more likely than men to say that they more often see people being kind or supportive. But the largest shares of both men (52%) and women (56%) say that they typically see an equal mix of supportive and bullying behavior on social media.

When asked about the efforts they see other users making to spread – or correct – misinformation, around two-thirds of users (63%) say they generally see an even mix of people trying to be deceptive and people trying to point out inaccurate information. Similar shares more often see one of these behaviors than others, with 18% of users saying they more often see people trying to be deceptive and 17% saying they more often see people trying to point out inaccurate information. Men are around twice as likely as women to say they more often seeing people being deceptive on social media (24% vs. 13%). But majorities of both men (58%) and women (67%) see an equal mix of deceptiveness and attempts to correct misinformation.

Users’ comfort level with social media companies using their personal data depends on how their data are used

The vast quantities of data that social media companies possess about their users – including behaviors, likes, clicks and other information users provide about themselves – are ultimately what allows these platforms to deliver individually targeted content in an automated fashion. And this survey finds that users’ comfort level with this behavior is heavily context-dependent. They are relatively accepting of their data being used for certain types of messages, but much less comfortable when it is used for other purposes.

Three-quarters of social media users find it acceptable for those platforms to use data about them and their online behavior to recommend events in their area that they might be interested in, while a smaller majority (57%) thinks it is acceptable if their data are used to recommend other people they might want to be friends with.

On the other hand, users are somewhat less comfortable with these sites using their data to show advertisements for products or services. Around half (52%) think this behavior is acceptable, but a similar share (47%) finds it to be not acceptable – and the share that finds it not at all acceptable (21%) is roughly double the share who finds it very acceptable (11%). Meanwhile, a substantial majority of users think it is not acceptable for social media platforms to use their data to deliver messages from political campaigns – and 31% say this is not acceptable at all.

Relatively sizable majorities of users across a range of age groups think it is acceptable for social media sites to use their data to show them events happening in their area. And majorities of users across a range of age categories feel it is not acceptable for social platforms to use their data to serve them ads from political campaigns.

But outside of these specific similarities, older users are much less accepting of social media sites using their data for other reasons. This is most pronounced when it comes to using that data to recommend other people they might know. By a two-to-one margin (66% to 33%), social media users ages 18 to 49 think this is an acceptable use of their data. But by a similar 63% to 36% margin, users ages 65 and older say this is not acceptable. Similarly, nearly six-in-ten users ages 18 to 49 think it is acceptable for these sites to use their data to show them advertisements for products or services, but that share falls to 39% among those 65 and older.

Beyond using their personal data in these specific ways, social media users express consistent and pronounced opposition to these platforms changing their sites in certain ways for some users but not others. Roughly eight-in-ten social media users think it is unacceptable for these platforms to do things like remind some users but not others to vote on election day (82%), or to show some users more of their friends’ happy posts and fewer of their sad posts (78%). And even the standard A/B testing that most platforms engage in on a continuous basis is viewed with much suspicion by users: 78% of users think it is unacceptable for social platforms to change the look and feel of their site for some users but not others.

Older users are overwhelmingly opposed to these interventions. But even among younger users, large shares find them problematic even in their most common forms. For instance, 71% of social media users ages 18 to 29 say it is unacceptable for these sites to change the look and feel for some users but not others.

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